In [4]:
pd.read_csv
In [1]:
df = pd.read_excel('AVM.xlsx', skiprows = 12)
df
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/xlrd/xlsx.py:246: PendingDeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
for elem in self.tree.getiterator():
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/xlrd/xlsx.py:292: PendingDeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
for elem in self.tree.getiterator():
Out[1]:
VA12
1
Unnamed: 2
2
29.2191780822
0
0.1
0.2
Unnamed: 8
Unnamed: 9
...
1.11
Unnamed: 55
0.10
0.11
Unnamed: 58
Unnamed: 59
1.49438356164
2.3
2.4
4.2
0
VA13
1
NaN
1
29.630137
0
0
0
NaN
NaN
...
0
3
1
2
NaN
NaN
1.272603
2
2
2
1
VA14
1
NaN
2
45.589041
0
0
0
NaN
NaN
...
1
NaN
2
NaN
NaN
NaN
1.331781
2
2
3
2
VA15
1
NaN
2
44.065753
0
0
0
NaN
NaN
...
NaN
2
NaN
NaN
NaN
0.951315
1
0
0
3
VA16
1
NaN
2
36.671233
0
0
0
NaN
NaN
...
1
NaN
2
0
NaN
NaN
0.843425
1
0
1
4
VA17
1
NaN
1
28.210959
1
1
1
NaN
1
...
1
NaN
NaN
NaN
NaN
NaN
0.924219
1
1
1
5
VA18
1
NaN
2
81.753425
0
0
0
NaN
NaN
...
1
NaN
NaN
NaN
NaN
NaN
1.815068
3
0
0
6
VA19
1
NaN
1
47.956164
0
0
0
NaN
NaN
...
1
NaN
NaN
NaN
NaN
NaN
1.439123
2
2
2
7
VA20
1
NaN
1
42.758904
0
0
0
NaN
NaN
...
0
3
1
0
NaN
NaN
1.555178
3
2
2
8
VA21
1
NaN
2
14.008219
0
1
1
NaN
1
...
1
NaN
NaN
NaN
NaN
NaN
0.370164
1
0
1
9
VA22
1
NaN
2
53.131507
0
0
0
NaN
NaN
...
1
NaN
1
2
NaN
NaN
1.352630
2
1
2
10
VA23
1
NaN
2
24.602740
0
0
0
NaN
NaN
...
1
NaN
3
5
NaN
NaN
0.852055
1
1
1
11
VA24
1
NaN
1
38.624658
0
1
1
1
2
...
0
2
0
NaN
NaN
NaN
1.582493
3
2
2
12
VA25
1
NaN
2
30.180822
0
0
0
NaN
NaN
...
0
3
3
NaN
NaN
NaN
0.903616
1
1
1
13
VA26
1
NaN
1
64.602740
0
0
0
NaN
NaN
...
0
2
0
0
NaN
NaN
1.392055
2
0
1
14
VA27
1
NaN
2
26.295890
0
0
0
NaN
NaN
...
1
NaN
1
NaN
NaN
NaN
0.725918
1
1
2
15
VA28
1
NaN
1
48.087671
0
0
0
NaN
NaN
...
NaN
NaN
1
NaN
NaN
NaN
1.271753
2
1
2
16
VA29
1
NaN
2
22.361644
1
0
0
NaN
NaN
...
1
NaN
0
0
NaN
NaN
0.567233
1
0
1
17
VA30
1
NaN
2
33.054795
0
1
1
2
1
...
NaN
0
0
NaN
NaN
1.131096
2
2
2
18
VA31
1
NaN
2
16.783562
0
1
1
1
1
...
0
2
1
2
NaN
NaN
0.865671
1
2
2
19
VA32
1
NaN
1
37.868493
0
0
0
NaN
NaN
...
NaN
2
NaN
NaN
NaN
0.927370
1
0
1
20
VA33
1
NaN
2
42.342466
1
1
1
1
1
...
0
NaN
NaN
3
NaN
NaN
1.386849
2
2
2
21
VA34
1
NaN
1
34.753425
0
1
1
1
1
...
NaN
1
1
NaN
NaN
1.235068
2
2
2
22
VA35
1
NaN
1
54.890411
0
0
0
NaN
NaN
...
NaN
NaN
0
0
NaN
NaN
1.417808
2
1
2
23
VA36
1
NaN
1
25.769863
0
1
1
NaN
NaN
...
1
NaN
NaN
NaN
NaN
NaN
0.645397
1
0
1
24
VA37
1
NaN
2
43.630137
0
1
1
1
1
...
1
NaN
3
3
NaN
NaN
1.492603
2
2
2
25
VA38
1
NaN
1
23.876712
0
0
0
NaN
NaN
...
NaN
0
NaN
NaN
NaN
0.777534
1
1
2
26
VA39
1
NaN
1
27.219178
0
0
0
NaN
NaN
...
0
NaN
0
3
NaN
NaN
1.064384
2
2
2
27
VA40
1
NaN
2
56.432877
0
0
0
NaN
NaN
...
NaN
NaN
0
NaN
NaN
NaN
1.638658
3
2
3
28
VA41
1
NaN
2
53.578082
0
1
3
NaN
NaN
...
1
NaN
0
NaN
NaN
NaN
1.281562
2
1
1
29
VA42
1
NaN
2
24.189041
0
1
2
1
2
...
0
NaN
3
NaN
NaN
NaN
0.583781
1
0
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1768
PA771
2
916
1
57.100000
1
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
0
0
1.262000
NaN
0
1
1769
PA772
2
917
2
31.500000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
0
0
0.920000
NaN
1
1
1770
PA773
2
918
1
60.000000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
0
0
1.370000
NaN
0
0
1771
PA774
2
919
2
22.700000
0
1
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
1
1.694000
NaN
2
2
1772
PA775
2
920
1
62.300000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
1
3
1.752800
NaN
2
3
1773
PA776
2
921
2
58.300000
1
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
1
3
1.692800
NaN
0
1
1774
PA777
2
922
2
56.100000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
0
0
2.034000
NaN
2
3
1775
PA778
2
923
2
77.000000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
2.102000
NaN
0
1
1776
PA779
2
924
2
61.200000
1
1
NaN
NaN
NaN
...
NaN
NaN
0
NaN
0
0
1.544000
NaN
1
2
1777
PA780
2
925
2
60.100000
0
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
1
1
2.226900
NaN
2
3
1778
PA781
2
926
2
22.800000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
0
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1.056000
NaN
0
1
1779
PA782
2
927
1
44.800000
0
0
NaN
NaN
NaN
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NaN
NaN
2
NaN
0
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1.486000
NaN
2
2
1780
PA783
2
928
1
23.500000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
1.410000
NaN
2
2
1781
PA784
2
929
1
49.000000
0
0
NaN
NaN
NaN
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NaN
NaN
2
NaN
1
1
1.065000
NaN
0
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1782
PA785
2
930
2
70.000000
0
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NaN
NaN
NaN
...
NaN
NaN
1
NaN
0
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1.650000
NaN
1
2
1783
PA786
2
931
1
74.700000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
1.784000
NaN
1
2
1784
PA787
2
932
2
42.400000
0
0
NaN
NaN
NaN
...
NaN
NaN
1
NaN
0
0
1.058000
NaN
1
1
1785
PA788
2
933
1
16.800000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
0
0
0.646000
NaN
1
1
1786
PA789
2
934
1
73.100000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
2.092000
NaN
2
3
1787
PA790
2
935
2
56.700000
0
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
1
1
1.309900
NaN
0
0
1788
PA791
2
936
1
65.800000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
1
1
2.004500
NaN
0
1
1789
PA792
2
937
1
65.300000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
1
1.888000
NaN
2
2
1790
PA793
2
938
1
38.800000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
1
1.256000
NaN
2
3
1791
PA794
2
939
2
27.400000
0
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NaN
NaN
NaN
...
NaN
NaN
3
NaN
0
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1.591700
NaN
2
3
1792
PA795
2
940
1
49.600000
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NaN
NaN
NaN
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NaN
NaN
3
NaN
0
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1.312000
NaN
1
1
1793
PA796
2
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1
11.400000
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3
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1
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0.858000
NaN
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1
1794
PA797
2
942
1
77.700000
0
0
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NaN
NaN
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NaN
NaN
2
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1
3
1.631000
NaN
0
1
1795
PA798
2
943
2
33.600000
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NaN
3
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1
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1.339000
NaN
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1
1796
PA799
2
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1
56.200000
1
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3
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1
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2.264000
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PA800
2
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2
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0.860000
NaN
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1
1798 rows × 64 columns
In [4]:
df
Out[4]:
VA12
1
Unnamed: 2
2
29.2191780822
0
0.1
0.2
Unnamed: 8
Unnamed: 9
...
1.11
Unnamed: 55
0.10
0.11
Unnamed: 58
Unnamed: 59
1.49438356164
2.3
2.4
4.2
0
VA13
1
NaN
1
29.630137
0
0
0
NaN
NaN
...
0
3
1
2
NaN
NaN
1.272603
2
2
2
1
VA14
1
NaN
2
45.589041
0
0
0
NaN
NaN
...
1
NaN
2
NaN
NaN
NaN
1.331781
2
2
3
2
VA15
1
NaN
2
44.065753
0
0
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...
NaN
2
NaN
NaN
NaN
0.951315
1
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0
3
VA16
1
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2
36.671233
0
0
0
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NaN
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1
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2
0
NaN
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0.843425
1
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1
4
VA17
1
NaN
1
28.210959
1
1
1
NaN
1
...
1
NaN
NaN
NaN
NaN
NaN
0.924219
1
1
1
5
VA18
1
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2
81.753425
0
0
0
NaN
NaN
...
1
NaN
NaN
NaN
NaN
NaN
1.815068
3
0
0
6
VA19
1
NaN
1
47.956164
0
0
0
NaN
NaN
...
1
NaN
NaN
NaN
NaN
NaN
1.439123
2
2
2
7
VA20
1
NaN
1
42.758904
0
0
0
NaN
NaN
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0
3
1
0
NaN
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1.555178
3
2
2
8
VA21
1
NaN
2
14.008219
0
1
1
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1
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1
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NaN
NaN
NaN
NaN
0.370164
1
0
1
9
VA22
1
NaN
2
53.131507
0
0
0
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NaN
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1
NaN
1
2
NaN
NaN
1.352630
2
1
2
10
VA23
1
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2
24.602740
0
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NaN
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1
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3
5
NaN
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0.852055
1
1
1
11
VA24
1
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1
38.624658
0
1
1
1
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0
2
0
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NaN
NaN
1.582493
3
2
2
12
VA25
1
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2
30.180822
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3
3
NaN
NaN
NaN
0.903616
1
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1
13
VA26
1
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1
64.602740
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2
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0
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1.392055
2
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14
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1
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2
26.295890
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1
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1
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NaN
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15
VA28
1
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1
48.087671
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1
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NaN
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1.271753
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1
2
16
VA29
1
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2
22.361644
1
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0
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NaN
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1
NaN
0
0
NaN
NaN
0.567233
1
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1
17
VA30
1
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2
33.054795
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1
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1
...
NaN
0
0
NaN
NaN
1.131096
2
2
2
18
VA31
1
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2
16.783562
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1
1
1
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0
2
1
2
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NaN
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2
19
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1
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1
37.868493
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NaN
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NaN
NaN
0.927370
1
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1
20
VA33
1
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2
42.342466
1
1
1
1
1
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3
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1.386849
2
2
2
21
VA34
1
NaN
1
34.753425
0
1
1
1
1
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NaN
1
1
NaN
NaN
1.235068
2
2
2
22
VA35
1
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1
54.890411
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1.417808
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23
VA36
1
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1
24
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43.630137
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1
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3
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2
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23.876712
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28
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57.100000
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2
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1773
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2
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1
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1.692800
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PA778
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1.486000
NaN
2
2
1780
PA783
2
928
1
23.500000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
1.410000
NaN
2
2
1781
PA784
2
929
1
49.000000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
1
1.065000
NaN
0
0
1782
PA785
2
930
2
70.000000
0
0
NaN
NaN
NaN
...
NaN
NaN
1
NaN
0
0
1.650000
NaN
1
2
1783
PA786
2
931
1
74.700000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
1.784000
NaN
1
2
1784
PA787
2
932
2
42.400000
0
0
NaN
NaN
NaN
...
NaN
NaN
1
NaN
0
0
1.058000
NaN
1
1
1785
PA788
2
933
1
16.800000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
0
0
0.646000
NaN
1
1
1786
PA789
2
934
1
73.100000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
2.092000
NaN
2
3
1787
PA790
2
935
2
56.700000
0
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
1
1
1.309900
NaN
0
0
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PA791
2
936
1
65.800000
0
0
NaN
NaN
NaN
...
NaN
NaN
0
NaN
1
1
2.004500
NaN
0
1
1789
PA792
2
937
1
65.300000
0
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NaN
NaN
NaN
...
NaN
NaN
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NaN
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1
1.888000
NaN
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2
1790
PA793
2
938
1
38.800000
0
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NaN
NaN
NaN
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NaN
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NaN
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3
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PA794
2
939
2
27.400000
0
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NaN
NaN
NaN
...
NaN
NaN
3
NaN
0
0
1.591700
NaN
2
3
1792
PA795
2
940
1
49.600000
0
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
0
0
1.312000
NaN
1
1
1793
PA796
2
941
1
11.400000
0
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
1
2
0.858000
NaN
0
1
1794
PA797
2
942
1
77.700000
0
0
NaN
NaN
NaN
...
NaN
NaN
2
NaN
1
3
1.631000
NaN
0
1
1795
PA798
2
943
2
33.600000
0
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
1
1
1.339000
NaN
0
1
1796
PA799
2
944
1
56.200000
1
0
NaN
NaN
NaN
...
NaN
NaN
3
NaN
1
3
2.264000
NaN
2
3
1797
PA800
2
945
2
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0
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NaN
NaN
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NaN
0
1
1798 rows × 64 columns
In [22]:
simple_features = [
'Sex',
'age',
'SM',
#'S_M (size)',
#'S-M (location)',
#'S-M (vein)',
'Max D',
'Volume (mL)',
'Hx of H',
'Embo',
'No drainv vein',
'Draining_Vein_Depth',
'Associated ',
'Max dose (Gy)',
'Peri_MarginalDose(Gy)',
'Isodose',
'Shots',
]
rename_dict = {
'Max dose (Gy)' : 'Max_Dose',
'age' : 'Age',
'Volume (mL)' : 'Volume',
'Associated ' : 'Aneurysm',
'Peri_MarginalDose(Gy)' : 'Marginal_Dose',
'Hx of H' : 'History_of_Hemorrhage',
'No drainv vein' : 'Number_Draining_Veins',
#'S_M (size)' : "SM_Size",
#'S-M (location)' : "SM_Location",
#'S-M (vein)' : "SM_Vein",
}
df_in = df[simple_features].copy()
df_in.rename(columns = rename_dict, inplace = True)
# log transform of age
#df_in['Age'] = np.log(df_in['Age'])
# first get rid of weird empty fields
empty_ric = ~(df['RIC'].str.strip().str.len().isnull())
ric_not_available = df['RIC'] == 9
# remove rows with missing data
bad = df_in.Volume.isnull() | df_in.Embo.isnull() | empty_ric | ric_not_available
good = ~bad
df_in = df_in[good]
# some patients experience radiation induced changes
# if this is encoded as 1 in 'RIC', then the time
# until change is in the column 'RIC post GK'.
# otherwise, look up their last MRI in 'MR FU'
ric = df['RIC'][good].astype(bool)
ric_true_time = df['RIC post GK'][good]
ric_false_time = df['MR FU'][good]
ric_time = ric_true_time.copy()
ric_false_mask = ~ric
ric_time[ric_false_mask] = ric_false_time[ric_false_mask]
# cast to a float only once both RIC times and last MRI followup are both in the
# same series, otherwise we'll get missing values
ric_time = ric_time.astype(float)
df_in['Early_RIC'] = ric & (ric_time <= 24)
print list(df_in.columns)
[u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
In [23]:
df_in
Out[23]:
Sex
Age
SM
Max D
Volume
History_of_Hemorrhage
Embo
Number_Draining_Veins
Draining_Vein_Depth
Aneurysm
Max_Dose
Marginal_Dose
Isodose
Shots
Early_RIC
0
1
43.704110
2
35
2.00
0
0
1
1
0
38
22
57.894737
3
False
1
2
35.879452
2
21
2.90
1
0
1
2
0
40
20
50.000000
2
True
2
2
21.197260
2
12
1.00
1
0
1
2
0
46
23
50.000000
2
False
3
1
25.800000
2
30
0.35
1
0
1
1
0
36
25
69.444444
1
True
4
2
31.010959
1
12
0.80
0
0
1
1
0
46
23
50.000000
2
False
5
2
63.016438
2
24
0.87
1
0
3
1
0
40
20
50.000000
1
True
6
1
44.320548
1
17
1.40
1
0
3
1
2
50
25
50.000000
2
False
7
2
40.202740
1
23
2.30
1
1
2
1
0
42
21
50.000000
3
False
9
1
36.638356
2
45
8.00
0
0
3
1
0
20
10
50.000000
4
False
10
2
24.328767
2
23
5.03
0
1
1
1
0
40
20
50.000000
2
False
11
2
29.219178
2
22
4.10
1
0
1
1
3
40
20
50.000000
4
True
12
1
29.630137
2
34
6.80
0
0
1
1
0
40
20
50.000000
2
False
13
2
45.589041
2
27
4.20
1
0
1
2
0
40
20
50.000000
3
False
14
2
44.065753
1
15
0.70
0
0
1
1
0
48
24
50.000000
2
False
15
2
36.671233
2
13
1.10
1
0
1
2
0
35
25
71.428571
1
True
17
2
81.753425
2
18
1.80
0
0
1
2
3
48
24
50.000000
2
False
18
1
47.956164
2
45
4.80
0
0
1
1
2
40
20
50.000000
2
False
19
1
42.758904
1
30
7.00
0
0
2
1
0
36
18
50.000000
2
True
20
2
14.008219
2
11
0.90
1
1
1
1
0
48
23
47.916667
3
True
21
2
53.131507
2
17
2.90
0
0
2
1
0
40
20
50.000000
2
True
22
2
24.602740
1
22
3.60
0
0
1
1
0
34
23
67.647059
1
True
23
1
38.624658
2
34
8.10
0
1
3
1
0
36
18
50.000000
4
False
24
2
30.180822
2
30
3.00
0
0
1
1
0
36
18
50.000000
2
True
25
1
64.602740
2
10
1.00
1
0
1
1
0
30
27
90.000000
1
False
26
2
26.295890
1
20
2.00
1
0
1
1
0
40
20
50.000000
3
True
27
1
48.087671
2
19
3.10
1
0
1
2
0
46
23
50.000000
2
False
28
2
22.361644
2
20
1.20
1
0
1
2
0
46
23
50.000000
2
False
29
2
33.054795
1
22
4.70
0
1
1
1
1
40
20
50.000000
4
True
30
2
16.783562
2
32
5.30
0
1
3
1
3
40
20
50.000000
3
False
31
1
37.868493
2
26
1.70
0
0
2
1
0
44
22
50.000000
4
False
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
978
1
62.767123
4
30
6.00
1
0
1
2
0
36
18
50.000000
3
True
979
1
10.709589
4
39
3.00
0
0
2
2
0
44
21
47.727273
5
True
980
2
18.854795
4
45
5.40
0
1
1
2
0
36
18
50.000000
2
True
981
1
7.052055
4
32
5.60
1
0
1
2
0
30
18
60.000000
2
False
982
1
11.098630
4
30
6.30
1
0
2
3
0
38
19
50.000000
2
False
983
2
45.372603
4
31
3.00
1
0
4
3
2
35
21
60.000000
3
False
984
2
36.978082
4
35
2.80
1
0
1
2
0
44
22
50.000000
6
False
985
2
15.010959
4
33
4.70
1
0
1
2
0
40
20
50.000000
3
False
986
1
5.739726
4
38
4.00
1
0
2
2
0
40
20
50.000000
1
False
987
2
22.906849
4
31
4.40
0
0
1
2
3
46
23
50.000000
4
True
988
2
21.515068
4
30
4.50
1
0
1
2
0
36
25
69.444444
2
True
989
2
15.561644
4
36
7.40
1
1
1
2
0
40
20
50.000000
3
False
990
1
9.953425
4
42
4.70
1
1
1
2
0
40
20
50.000000
4
False
992
1
13.726027
4
35
5.80
1
0
2
2
0
44
22
50.000000
6
False
993
1
20.290411
4
30
7.20
1
0
1
2
0
34
24
70.588235
2
False
994
2
12.246575
4
34
4.60
0
1
1
2
0
40
20
50.000000
3
True
995
1
23.416438
4
30
11.00
1
0
1
2
0
35
25
71.428571
2
True
996
2
16.484932
4
40
24.00
1
0
2
2
0
40
20
50.000000
6
False
997
1
6.386301
4
32
1.68
1
1
1
2
0
20
18
90.000000
1
True
998
2
35.249315
4
35
6.10
1
0
1
2
0
36
18
50.000000
2
True
999
1
13.578082
4
35
4.10
1
0
1
2
0
38
19
50.000000
2
True
1000
1
7.978082
4
36
3.90
1
0
1
2
0
36
22
61.111111
3
True
1001
2
11.824658
4
35
3.30
1
0
1
2
0
28
20
71.428571
3
True
1002
2
4.731507
4
47
12.70
0
0
5
3
0
44
22
50.000000
5
True
1003
2
56.849315
4
30
5.50
0
0
3
3
0
42
21
50.000000
3
True
1004
1
41.545205
4
31
5.50
0
0
1
2
0
40
20
50.000000
3
False
1005
2
14.950685
4
50
5.00
1
1
1
2
0
50
25
50.000000
3
False
1006
2
27.816438
4
34
5.90
0
0
1
2
0
34
17
50.000000
4
True
1008
1
20.484932
4
34
6.00
1
0
1
2
0
28
20
71.428571
2
True
1009
2
10.898630
4
31
2.90
1
0
1
2
0
40
20
50.000000
3
False
958 rows × 15 columns
In [24]:
outcomes = [
'Final_Result',
'RIC',
'Degree of RIC',
'K-Mtime yrs',
'Permanet',
'S/S from RIC',
'Post GK H',
]
In [25]:
df_out = df[outcomes].rename(columns = {
'Degree of RIC' : 'RIC_Degree',
# either last time patient had an angiogram or time until positive angiogram
'K-Mtime yrs': 'Years',
'Permanet' : 'RIC_Permanent',
'S/S from RIC' : 'RIC_Symptoms',
'Post GK H' : 'Hemorrhage'}).copy()
df_out['RIC'] = df_out['RIC'].convert_objects(convert_numeric=True)
df_out['RIC_Symptoms'] = df_out['RIC_Symptoms'].convert_objects(convert_numeric=True)
df_out['RIC_Symptoms'][df_out.RIC_Symptoms == 0] = NaN
# if only values should be False/True
df_out['RIC_Permanent'] = df_out['RIC_Permanent'].convert_objects(convert_numeric=True)
df_out['RIC_Permanent'][df_out['RIC_Permanent'].isnull()] = 0
df_out['RIC_Permanent'] = df_out['RIC_Permanent'].astype('bool')
# absence of data indicated either by 9 or NaN, normalize to only use one
df_out['RIC'][df_out['RIC'] == 9] = np.nan
df_out['RIC_Degree'] = df_out['RIC_Degree'].convert_objects(convert_numeric=True)
df_out['Hemorrhage'] = df_out['Hemorrhage'] > 0
In [26]:
df_out['Angio_Obliteration'] = df_out['Final_Result'] == 4
In [27]:
df_out['Obliteration'] = (df_out['Final_Result'] == 4) | (df_out['Final_Result'] == 5)
In [28]:
TIME = 4
print "# Years <= TIME", (df_out.Years <= TIME).sum()
before = df_out.Years <= TIME
censored = before & ~df_out.Obliteration
df_out['Censored'] = censored
print "# censored", censored.sum()
Y_RIC = ( (df_out.RIC == 1) & ~df_out.RIC_Symptoms.isnull() )[~bad & ~censored]
print "Mean RIC", Y_RIC.mean()
Y_Obliteration = np.array((before & df_out.Obliteration)[~bad & ~censored])
print "Mean Oblit", Y_Obliteration.mean()
# Years <= TIME 603
# censored 100
Mean RIC 0.109048723898
Mean Oblit 0.551044083527
In [29]:
df_out['RIC_Bad'] = ((df_out.RIC == 1) & ~df_out.RIC_Symptoms.isnull() & df_out.RIC_Permanent)
df_out['Adverse_Event'] = df_out.Hemorrhage | df_out.RIC_Bad
df_out['Obliteration_Healthy'] = df_out.Obliteration & ~df_out.Adverse_Event
df_in.to_csv("avm_features.csv")
df_out.to_csv("avm_outcomes.csv")
In [30]:
X = df_in.as_matrix()
print "X before filtering", X.dtype, X.shape
mask = ~censored[~bad]
X = np.array([X[i] for i, b in enumerate(mask) if b]).astype(float)
print "X after filtering", X.dtype, X.shape
X before filtering object (958, 15)
X after filtering float64 (862, 15)
In [31]:
n_samples, n_features = X.shape
In [31]:
In [32]:
import sklearn.linear_model
import sklearn.ensemble
import sklearn.svm
import sklearn.neighbors
# dictionary mapping model instances to their CV parameter grids
candidate_models = {}
Cs = [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10]
gammas = [0.0001, .0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]
svm_param_grids = [
# linear SVM
{
'C' : Cs,
'kernel' : ['linear'],
'probability' : [True],
},
# RBF SVM
{
'C' : Cs,
'gamma': gammas,
'kernel' : ['rbf'],
'probability' : [True],
}
]
svm = sklearn.svm.SVC()
candidate_models[svm] = svm_param_grids
knn_params = [
{
'n_neighbors' : [1,3,5,15,31],
'weights' : ['uniform', 'distance'],
}
]
knn = sklearn.neighbors.KNeighborsClassifier()
candidate_models[knn] = knn_params
from sklearn.neighbors import KNeighborsClassifier
lr_parameter_grids = [
{
'penalty' : ['l1', 'l2'],
'fit_intercept' : [True, False],
'C' : Cs,
}
]
lr = sklearn.linear_model.LogisticRegression()
candidate_models[lr] = lr_parameter_grids
n_estimators = [25, 50, 100, 200, 400]
ada = sklearn.ensemble.AdaBoostClassifier()
candidate_models[ada] = [{ 'n_estimators' : n_estimators, 'learning_rate' : [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1] }]
criterions = ['gini', 'entropy']
max_depth = [10, 20, None]
rf_parameter_grids = [
{
'criterion' : criterions,
'max_depth' : max_depth,
'n_estimators' : n_estimators,
}
]
extra = sklearn.ensemble.ExtraTreesClassifier()
rf = sklearn.ensemble.RandomForestClassifier()
candidate_models[extra] = rf_parameter_grids
candidate_models[rf] = rf_parameter_grids
print "# candidate model sets:", len(candidate_models)
for k,v in candidate_models.iteritems():
print
print k
print "---"
print v
print
# candidate model sets: 6
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
---
[{'n_neighbors': [1, 3, 5, 15, 31], 'weights': ['uniform', 'distance']}]
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
---
[{'kernel': ['linear'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True]}, {'kernel': ['rbf'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]}]
ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
---
[{'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}]
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
---
[{'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'fit_intercept': [True, False]}]
RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
---
[{'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}]
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=50, random_state=None)
---
[{'n_estimators': [25, 50, 100, 200, 400], 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]}]
In [33]:
reload(cv)
import cv
cv_aucs = cv.evaluate(X, Y_Obliteration, candidate_models)
for k, aucs in cv_aucs.iteritems():
print k
print "AUCs", aucs
print "mean AUC: %0.4f" % np.mean(aucs)
print "median AUC: %0.4f" % np.median(aucs)
print "std AUC: %0.4f" % np.std(aucs)
Fold #1
# total train 689
# test 173
Base Model KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
Param_grid #1/1 {'n_neighbors': [1, 3, 5, 15, 31], 'weights': ['uniform', 'distance']}
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='uniform')
-- AUC: 0.5662
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='distance')
-- AUC: 0.5662
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='uniform')
-- AUC: 0.6024
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='distance')
-- AUC: 0.5966
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
-- AUC: 0.6360
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='distance')
-- AUC: 0.6288
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='uniform')
-- AUC: 0.6800
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='distance')
-- AUC: 0.6727
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- AUC: 0.7069
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='distance')
-- AUC: 0.7027
== Best Model for KNeighborsClassifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform'), AUC = 0.7069
Base Model SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
Param_grid #1/2 {'kernel': ['linear'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7331
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7328
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7290
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7291
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7274
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7249
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7223
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7209
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7220
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7213
Param_grid #2/2 {'kernel': ['rbf'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7322
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6764
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7319
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7322
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7314
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7201
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7055
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6101
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5431
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=10, kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4969
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7323
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6202
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7326
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7332
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6752
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6649
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7034
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6041
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5861
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4923
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7319
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7328
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7330
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6768
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7321
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7182
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7034
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6360
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5385
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5003
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6763
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6762
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7329
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6509
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7321
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7181
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7035
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6349
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5695
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5098
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6772
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6521
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6206
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7333
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7324
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7191
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7037
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6322
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5695
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5080
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6773
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7332
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7329
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7333
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7312
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7180
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7040
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5958
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5715
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5080
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6679
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7333
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7327
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7289
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7275
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7129
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6978
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6351
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5572
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5064
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6207
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7326
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7320
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7298
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7248
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7079
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6849
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6194
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5833
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5080
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7329
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7289
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7305
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7265
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7184
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6537
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6218
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5827
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5304
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5091
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7321
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7298
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7278
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7221
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7057
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6239
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5956
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5793
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5696
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5072
== Best Model for SVC = SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False), AUC = 0.7333
Base Model ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6980
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6545
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6544
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6944
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6616
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6370
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6993
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6594
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6619
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7061
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6663
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6536
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6970
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6618
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6613
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7014
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6640
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6686
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7065
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6672
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6641
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7043
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6658
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6589
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6980
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6693
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6630
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7056
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6649
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6638
== Best Model for ExtraTreesClassifier = ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7065
Base Model LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
Param_grid #1/1 {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'fit_intercept': [True, False]}
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5953
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5953
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7259
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7259
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7278
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7279
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7252
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7255
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7249
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7249
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7247
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7250
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7247
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7252
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7313
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7313
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7313
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7312
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7299
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7299
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7297
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7300
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7262
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7270
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7263
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7269
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7256
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7260
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7255
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7254
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7248
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7254
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7248
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7252
== Best Model for LogisticRegression = LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001), AUC = 0.7313
Base Model RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6775
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6660
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6866
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6927
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6919
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6822
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6926
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6907
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6927
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7004
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6963
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7018
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7080
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6928
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6881
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6994
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6906
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6909
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6990
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6950
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6942
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7081
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6918
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6989
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7002
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6943
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6947
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7078
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6986
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6984
== Best Model for RandomForestClassifier = RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7081
Base Model AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=50, random_state=None)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]}
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=25, random_state=None)
-- AUC: 0.6742
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=25, random_state=None)
-- AUC: 0.6760
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=25, random_state=None)
-- AUC: 0.6887
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=25, random_state=None)
-- AUC: 0.7194
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None)
-- AUC: 0.7316
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=25, random_state=None)
-- AUC: 0.6987
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=25, random_state=None)
-- AUC: 0.6749
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=50, random_state=None)
-- AUC: 0.6742
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=50, random_state=None)
-- AUC: 0.6876
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=50, random_state=None)
-- AUC: 0.7107
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None)
-- AUC: 0.7299
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=50, random_state=None)
-- AUC: 0.7264
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
-- AUC: 0.6916
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=50, random_state=None)
-- AUC: 0.6678
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=100, random_state=None)
-- AUC: 0.6760
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=100, random_state=None)
-- AUC: 0.7108
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=100, random_state=None)
-- AUC: 0.7139
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=100, random_state=None)
-- AUC: 0.7251
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=100, random_state=None)
-- AUC: 0.7116
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=100, random_state=None)
-- AUC: 0.6721
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=100, random_state=None)
-- AUC: 0.6452
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=200, random_state=None)
-- AUC: 0.6887
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=200, random_state=None)
-- AUC: 0.7134
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=200, random_state=None)
-- AUC: 0.7287
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=200, random_state=None)
-- AUC: 0.7119
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=200, random_state=None)
-- AUC: 0.6974
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=200, random_state=None)
-- AUC: 0.6604
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=200, random_state=None)
-- AUC: 0.6377
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=400, random_state=None)
-- AUC: 0.7120
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=400, random_state=None)
-- AUC: 0.7284
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- AUC: 0.7301
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=400, random_state=None)
-- AUC: 0.6971
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=400, random_state=None)
-- AUC: 0.6811
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=400, random_state=None)
-- AUC: 0.6512
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=400, random_state=None)
-- AUC: 0.6230
== Best Model for AdaBoostClassifier = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None), AUC = 0.7316
Done with hyperparameter selection
==========
LogisticRegression
-- best model: LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- params: {'penalty': 'l2', 'C': 0.001, 'fit_intercept': True}
-- Fold #1 test AUC for LogisticRegression: 0.7273
ExtraTreesClassifier
-- best model: ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 200, 'criterion': 'gini', 'max_depth': 10}
-- Fold #1 test AUC for ExtraTreesClassifier: 0.7346
RandomForestClassifier
-- best model: RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 200, 'criterion': 'entropy', 'max_depth': 10}
-- Fold #1 test AUC for RandomForestClassifier: 0.7338
KNeighborsClassifier
-- best model: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- params: {'n_neighbors': 31, 'weights': 'uniform'}
-- Fold #1 test AUC for KNeighborsClassifier: 0.7352
AdaBoostClassifier
-- best model: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None)
-- params: {'n_estimators': 25, 'learning_rate': 0.1}
-- Fold #1 test AUC for AdaBoostClassifier: 0.6839
SVC
-- best model: SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- params: {'kernel': 'rbf', 'C': 0.5, 'gamma': 0.0005, 'probability': True}
-- Fold #1 test AUC for SVC: 0.7346
Fold #2
# total train 689
# test 173
Base Model KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
Param_grid #1/1 {'n_neighbors': [1, 3, 5, 15, 31], 'weights': ['uniform', 'distance']}
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='uniform')
-- AUC: 0.5408
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='distance')
-- AUC: 0.5408
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='uniform')
-- AUC: 0.6147
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='distance')
-- AUC: 0.6108
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
-- AUC: 0.6374
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='distance')
-- AUC: 0.6344
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='uniform')
-- AUC: 0.6831
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='distance')
-- AUC: 0.6821
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- AUC: 0.7086
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='distance')
-- AUC: 0.7041
== Best Model for KNeighborsClassifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform'), AUC = 0.7086
Base Model SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
Param_grid #1/2 {'kernel': ['linear'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6312
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7274
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7224
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7162
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7160
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7156
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7162
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7159
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7148
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7151
Param_grid #2/2 {'kernel': ['rbf'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6782
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6292
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7258
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7260
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6763
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7265
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7165
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6585
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6147
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=10, kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5339
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7268
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7252
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7268
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6775
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7262
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7158
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6592
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5971
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5300
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6292
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6788
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7270
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7269
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7254
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7158
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6582
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6118
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5193
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7267
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7267
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6737
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6281
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7251
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7158
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6588
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6123
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4833
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7278
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7269
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5558
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7272
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7268
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7277
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7157
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6597
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6103
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4851
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7272
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7272
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7266
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7273
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7134
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6062
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6105
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5112
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7272
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7274
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7276
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7250
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7223
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7199
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7061
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6573
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5783
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4591
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7281
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7271
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7207
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7204
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7106
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6921
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6442
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6120
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4811
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7279
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7233
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7171
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7165
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7098
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6605
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6338
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6141
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5959
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5231
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7273
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7167
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7170
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7134
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7002
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6363
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6213
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6094
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5977
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4645
== Best Model for SVC = SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False), AUC = 0.7281
Base Model ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6895
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6599
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6586
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6913
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6904
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6600
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6836
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6589
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6678
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6904
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6714
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6684
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6866
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6775
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6728
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6916
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6836
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6715
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6960
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6703
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6726
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6910
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6773
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6745
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6893
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6746
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6730
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6920
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6738
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6738
== Best Model for ExtraTreesClassifier = ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.6960
Base Model LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
Param_grid #1/1 {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'fit_intercept': [True, False]}
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5489
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5489
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7160
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7169
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7206
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7212
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7167
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7170
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7165
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7170
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7137
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7153
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7134
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7153
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7239
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7239
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7231
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7231
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7217
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7220
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7206
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7207
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7197
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7203
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7188
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7193
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7169
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7175
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7159
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7172
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7145
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7166
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7139
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7158
== Best Model for LogisticRegression = LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001), AUC = 0.7239
Base Model RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6803
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6799
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6881
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6960
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6924
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7011
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7021
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6931
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6840
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6934
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6945
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6928
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7071
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6961
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6945
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7067
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6926
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6923
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7009
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6896
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6882
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7035
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7026
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6933
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7017
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6995
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6933
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7022
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6972
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6948
== Best Model for RandomForestClassifier = RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7071
Base Model AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=50, random_state=None)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]}
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=25, random_state=None)
-- AUC: 0.6471
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=25, random_state=None)
-- AUC: 0.6612
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=25, random_state=None)
-- AUC: 0.6649
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=25, random_state=None)
-- AUC: 0.6870
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None)
-- AUC: 0.7027
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=25, random_state=None)
-- AUC: 0.6842
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=25, random_state=None)
-- AUC: 0.6782
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=50, random_state=None)
-- AUC: 0.6504
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=50, random_state=None)
-- AUC: 0.6637
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=50, random_state=None)
-- AUC: 0.6757
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None)
-- AUC: 0.7036
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=50, random_state=None)
-- AUC: 0.7115
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
-- AUC: 0.6675
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=50, random_state=None)
-- AUC: 0.6456
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=100, random_state=None)
-- AUC: 0.6612
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=100, random_state=None)
-- AUC: 0.6749
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=100, random_state=None)
-- AUC: 0.6829
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=100, random_state=None)
-- AUC: 0.7119
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=100, random_state=None)
-- AUC: 0.7011
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=100, random_state=None)
-- AUC: 0.6497
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=100, random_state=None)
-- AUC: 0.6333
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=200, random_state=None)
-- AUC: 0.6623
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=200, random_state=None)
-- AUC: 0.6838
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=200, random_state=None)
-- AUC: 0.7007
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=200, random_state=None)
-- AUC: 0.7019
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=200, random_state=None)
-- AUC: 0.6799
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=200, random_state=None)
-- AUC: 0.6351
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=200, random_state=None)
-- AUC: 0.6261
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=400, random_state=None)
-- AUC: 0.6759
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=400, random_state=None)
-- AUC: 0.7006
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- AUC: 0.7135
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=400, random_state=None)
-- AUC: 0.6807
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=400, random_state=None)
-- AUC: 0.6639
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=400, random_state=None)
-- AUC: 0.6289
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=400, random_state=None)
-- AUC: 0.6157
== Best Model for AdaBoostClassifier = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None), AUC = 0.7135
Done with hyperparameter selection
==========
LogisticRegression
-- best model: LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- params: {'penalty': 'l2', 'C': 0.0001, 'fit_intercept': True}
-- Fold #2 test AUC for LogisticRegression: 0.7377
ExtraTreesClassifier
-- best model: ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 200, 'criterion': 'gini', 'max_depth': 10}
-- Fold #2 test AUC for ExtraTreesClassifier: 0.7129
RandomForestClassifier
-- best model: RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10}
-- Fold #2 test AUC for RandomForestClassifier: 0.7209
KNeighborsClassifier
-- best model: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- params: {'n_neighbors': 31, 'weights': 'uniform'}
-- Fold #2 test AUC for KNeighborsClassifier: 0.7091
AdaBoostClassifier
-- best model: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- params: {'n_estimators': 400, 'learning_rate': 0.01}
-- Fold #2 test AUC for AdaBoostClassifier: 0.7424
SVC
-- best model: SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.0005, 'probability': True}
-- Fold #2 test AUC for SVC: 0.7380
Fold #3
# total train 690
# test 172
Base Model KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
Param_grid #1/1 {'n_neighbors': [1, 3, 5, 15, 31], 'weights': ['uniform', 'distance']}
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='uniform')
-- AUC: 0.5504
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='distance')
-- AUC: 0.5504
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='uniform')
-- AUC: 0.6037
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='distance')
-- AUC: 0.6008
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
-- AUC: 0.6351
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='distance')
-- AUC: 0.6304
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='uniform')
-- AUC: 0.6684
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='distance')
-- AUC: 0.6669
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- AUC: 0.6768
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='distance')
-- AUC: 0.6767
== Best Model for KNeighborsClassifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform'), AUC = 0.6768
Base Model SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
Param_grid #1/2 {'kernel': ['linear'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7058
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7064
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7005
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6948
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6878
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6845
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6835
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6834
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6835
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6837
Param_grid #2/2 {'kernel': ['rbf'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7070
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6575
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7073
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7060
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7068
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7000
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6835
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6170
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5763
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=10, kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5283
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7071
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7086
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6554
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7069
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7070
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6998
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6832
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6091
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5751
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5063
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7071
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7084
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7070
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7053
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7064
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6996
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6851
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6048
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5743
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4853
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7063
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7073
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7065
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7060
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7061
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6996
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6850
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6045
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5744
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4725
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7063
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7061
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7065
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7065
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7061
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7000
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6853
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6054
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5738
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4773
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7067
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7063
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7064
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7066
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7073
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7025
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6874
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6036
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5742
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4756
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7063
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7062
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7061
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7025
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7000
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6869
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6784
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6052
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5740
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4869
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7064
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7062
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7070
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6973
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6933
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6748
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6637
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5998
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5753
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4762
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7063
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7005
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6952
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6841
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6697
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6396
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6128
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5831
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5699
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5003
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7068
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6949
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6909
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6769
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6562
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6197
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5947
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5812
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5695
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5180
== Best Model for SVC = SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False), AUC = 0.7086
Base Model ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6543
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6315
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6296
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6534
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6365
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6292
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6549
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6239
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6281
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6631
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6339
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6290
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6578
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6372
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6383
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6664
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6403
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6386
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6599
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6307
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6431
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6685
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6377
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6315
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6644
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6368
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6344
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6701
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6401
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6328
== Best Model for ExtraTreesClassifier = ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.6701
Base Model LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
Param_grid #1/1 {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'fit_intercept': [True, False]}
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5254
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5254
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6981
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6987
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7011
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7015
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6920
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6928
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6913
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6929
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6886
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6901
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6881
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6896
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7073
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7073
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7056
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7056
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7045
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7047
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7023
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7028
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6959
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6974
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6942
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6954
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6909
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6925
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6900
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6920
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6887
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6904
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6881
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.6898
== Best Model for LogisticRegression = LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001), AUC = 0.7073
Base Model RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6538
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6447
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6302
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6569
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6451
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6618
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6728
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6580
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6601
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6562
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6439
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6611
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6683
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6571
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6629
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6735
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6540
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6676
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6669
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6640
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6597
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6672
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6648
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6539
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6735
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6577
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6630
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6752
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6659
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6608
== Best Model for RandomForestClassifier = RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.6752
Base Model AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=50, random_state=None)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]}
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=25, random_state=None)
-- AUC: 0.5856
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=25, random_state=None)
-- AUC: 0.6189
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=25, random_state=None)
-- AUC: 0.6351
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=25, random_state=None)
-- AUC: 0.6788
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None)
-- AUC: 0.6974
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=25, random_state=None)
-- AUC: 0.6633
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=25, random_state=None)
-- AUC: 0.6408
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=50, random_state=None)
-- AUC: 0.6033
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=50, random_state=None)
-- AUC: 0.6384
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=50, random_state=None)
-- AUC: 0.6678
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None)
-- AUC: 0.6970
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=50, random_state=None)
-- AUC: 0.6954
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
-- AUC: 0.6530
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=50, random_state=None)
-- AUC: 0.6404
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=100, random_state=None)
-- AUC: 0.6030
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=100, random_state=None)
-- AUC: 0.6693
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=100, random_state=None)
-- AUC: 0.6768
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=100, random_state=None)
-- AUC: 0.6964
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=100, random_state=None)
-- AUC: 0.6789
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=100, random_state=None)
-- AUC: 0.6325
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=100, random_state=None)
-- AUC: 0.6215
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=200, random_state=None)
-- AUC: 0.6193
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=200, random_state=None)
-- AUC: 0.6767
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=200, random_state=None)
-- AUC: 0.6924
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=200, random_state=None)
-- AUC: 0.6799
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=200, random_state=None)
-- AUC: 0.6660
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=200, random_state=None)
-- AUC: 0.6196
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=200, random_state=None)
-- AUC: 0.6088
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=400, random_state=None)
-- AUC: 0.6598
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=400, random_state=None)
-- AUC: 0.6925
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- AUC: 0.7007
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=400, random_state=None)
-- AUC: 0.6669
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=400, random_state=None)
-- AUC: 0.6509
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=400, random_state=None)
-- AUC: 0.6069
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=400, random_state=None)
-- AUC: 0.5971
== Best Model for AdaBoostClassifier = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None), AUC = 0.7007
Done with hyperparameter selection
==========
LogisticRegression
-- best model: LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- params: {'penalty': 'l2', 'C': 0.0001, 'fit_intercept': True}
-- Fold #3 test AUC for LogisticRegression: 0.8167
ExtraTreesClassifier
-- best model: ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 400, 'criterion': 'entropy', 'max_depth': 10}
-- Fold #3 test AUC for ExtraTreesClassifier: 0.8289
RandomForestClassifier
-- best model: RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 400, 'criterion': 'entropy', 'max_depth': 10}
-- Fold #3 test AUC for RandomForestClassifier: 0.8294
KNeighborsClassifier
-- best model: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- params: {'n_neighbors': 31, 'weights': 'uniform'}
-- Fold #3 test AUC for KNeighborsClassifier: 0.8329
AdaBoostClassifier
-- best model: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- params: {'n_estimators': 400, 'learning_rate': 0.01}
-- Fold #3 test AUC for AdaBoostClassifier: 0.8026
SVC
-- best model: SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- params: {'kernel': 'rbf', 'C': 0.001, 'gamma': 0.0005, 'probability': True}
-- Fold #3 test AUC for SVC: 0.8187
Fold #4
# total train 690
# test 172
Base Model KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
Param_grid #1/1 {'n_neighbors': [1, 3, 5, 15, 31], 'weights': ['uniform', 'distance']}
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='uniform')
-- AUC: 0.5711
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='distance')
-- AUC: 0.5711
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='uniform')
-- AUC: 0.6447
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='distance')
-- AUC: 0.6402
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
-- AUC: 0.6677
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='distance')
-- AUC: 0.6661
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='uniform')
-- AUC: 0.6988
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='distance')
-- AUC: 0.7000
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- AUC: 0.7233
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='distance')
-- AUC: 0.7232
== Best Model for KNeighborsClassifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform'), AUC = 0.7233
Base Model SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
Param_grid #1/2 {'kernel': ['linear'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5544
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7259
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7220
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7199
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7075
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7084
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7037
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7030
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7012
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7026
Param_grid #2/2 {'kernel': ['rbf'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6186
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6930
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5881
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6184
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7224
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6694
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6301
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5789
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5791
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=10, kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5160
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6187
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6193
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6709
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6165
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7249
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7247
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6608
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5727
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5513
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5249
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6188
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6701
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7246
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6172
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6707
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6329
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7133
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5747
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5515
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5201
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6090
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6714
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6707
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6324
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7254
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7238
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6244
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5753
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5774
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5038
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7244
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7241
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6180
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7255
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7253
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7248
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7133
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5433
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5758
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4932
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6101
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6691
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7247
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7264
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7289
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7265
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7157
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6029
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5769
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4635
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6643
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7246
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7257
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7243
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7247
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7192
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7127
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6614
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6019
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4866
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6181
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7256
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7249
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7225
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7215
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7098
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7058
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6470
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5965
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4616
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7260
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7220
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7213
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7179
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7151
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6988
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6778
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5996
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5859
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4897
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7251
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7207
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7158
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7171
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7106
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6913
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6570
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6002
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5847
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4586
== Best Model for SVC = SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False), AUC = 0.7289
Base Model ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7017
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6738
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6665
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7036
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6732
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6774
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7036
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6837
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6949
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7025
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6780
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6732
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7083
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6822
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6890
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7091
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6907
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6907
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7128
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6875
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6891
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7161
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6884
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6940
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7128
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6902
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6933
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7147
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6917
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6912
== Best Model for ExtraTreesClassifier = ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7161
Base Model LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
Param_grid #1/1 {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'fit_intercept': [True, False]}
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5484
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5484
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7163
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7163
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7213
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7209
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7173
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7167
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7152
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7154
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7142
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7141
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7141
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7145
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7235
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7235
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7242
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7242
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7239
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7239
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7234
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7236
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7210
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7206
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7188
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7188
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7164
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7158
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7156
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7156
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7147
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7149
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7144
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7147
== Best Model for LogisticRegression = LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001), AUC = 0.7242
Base Model RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6966
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6878
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6993
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6957
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6940
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6888
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7041
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7022
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6826
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7104
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6982
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6984
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6997
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7051
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6908
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7100
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7014
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7151
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7076
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7057
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7026
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7115
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7099
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7041
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7091
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7034
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7090
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7134
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7024
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7113
== Best Model for RandomForestClassifier = RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7151
Base Model AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=50, random_state=None)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]}
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=25, random_state=None)
-- AUC: 0.6110
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=25, random_state=None)
-- AUC: 0.6272
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=25, random_state=None)
-- AUC: 0.6689
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=25, random_state=None)
-- AUC: 0.6957
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None)
-- AUC: 0.7091
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=25, random_state=None)
-- AUC: 0.6881
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=25, random_state=None)
-- AUC: 0.6758
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=50, random_state=None)
-- AUC: 0.6110
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=50, random_state=None)
-- AUC: 0.6687
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=50, random_state=None)
-- AUC: 0.6777
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None)
-- AUC: 0.7087
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=50, random_state=None)
-- AUC: 0.7127
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
-- AUC: 0.6773
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=50, random_state=None)
-- AUC: 0.6681
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=100, random_state=None)
-- AUC: 0.6257
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=100, random_state=None)
-- AUC: 0.6777
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=100, random_state=None)
-- AUC: 0.6902
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=100, random_state=None)
-- AUC: 0.7144
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=100, random_state=None)
-- AUC: 0.7046
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=100, random_state=None)
-- AUC: 0.6698
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=100, random_state=None)
-- AUC: 0.6565
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=200, random_state=None)
-- AUC: 0.6661
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=200, random_state=None)
-- AUC: 0.6900
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=200, random_state=None)
-- AUC: 0.7057
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=200, random_state=None)
-- AUC: 0.7040
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=200, random_state=None)
-- AUC: 0.6925
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=200, random_state=None)
-- AUC: 0.6562
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=200, random_state=None)
-- AUC: 0.6423
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=400, random_state=None)
-- AUC: 0.6745
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=400, random_state=None)
-- AUC: 0.7055
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- AUC: 0.7152
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=400, random_state=None)
-- AUC: 0.6944
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=400, random_state=None)
-- AUC: 0.6857
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=400, random_state=None)
-- AUC: 0.6481
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=400, random_state=None)
-- AUC: 0.6238
== Best Model for AdaBoostClassifier = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None), AUC = 0.7152
Done with hyperparameter selection
==========
LogisticRegression
-- best model: LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- params: {'penalty': 'l2', 'C': 0.001, 'fit_intercept': True}
-- Fold #4 test AUC for LogisticRegression: 0.7584
ExtraTreesClassifier
-- best model: ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 200, 'criterion': 'entropy', 'max_depth': 10}
-- Fold #4 test AUC for ExtraTreesClassifier: 0.7300
RandomForestClassifier
-- best model: RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 100, 'criterion': 'entropy', 'max_depth': None}
-- Fold #4 test AUC for RandomForestClassifier: 0.7392
KNeighborsClassifier
-- best model: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- params: {'n_neighbors': 31, 'weights': 'uniform'}
-- Fold #4 test AUC for KNeighborsClassifier: 0.6962
AdaBoostClassifier
-- best model: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- params: {'n_estimators': 400, 'learning_rate': 0.01}
-- Fold #4 test AUC for AdaBoostClassifier: 0.7421
SVC
-- best model: SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.01, 'probability': True}
-- Fold #4 test AUC for SVC: 0.7511
Fold #5
# total train 690
# test 172
Base Model KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
Param_grid #1/1 {'n_neighbors': [1, 3, 5, 15, 31], 'weights': ['uniform', 'distance']}
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='uniform')
-- AUC: 0.5553
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=1, p=2, weights='distance')
-- AUC: 0.5553
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='uniform')
-- AUC: 0.6094
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=3, p=2, weights='distance')
-- AUC: 0.6081
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='uniform')
-- AUC: 0.6488
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=5, p=2, weights='distance')
-- AUC: 0.6442
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='uniform')
-- AUC: 0.6913
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=15, p=2, weights='distance')
-- AUC: 0.6883
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- AUC: 0.7098
-- KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='distance')
-- AUC: 0.7059
== Best Model for KNeighborsClassifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform'), AUC = 0.7098
Base Model SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
Param_grid #1/2 {'kernel': ['linear'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6292
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7300
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='linear', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7250
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7192
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7168
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7154
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7129
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7132
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7127
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='linear', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7125
Param_grid #2/2 {'kernel': ['rbf'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'probability': [True], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 10]}
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6782
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6901
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6290
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6531
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7288
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7332
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6317
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5999
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6041
-- SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=10, kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4996
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7285
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6786
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6286
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6799
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6800
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6829
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6330
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5972
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5597
-- SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5075
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7284
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6283
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6294
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6293
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7308
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7327
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.1, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6767
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6327
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5745
-- SVC(C=0.005, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4813
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6774
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6801
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6287
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7308
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6545
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7327
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7220
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6643
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6014
-- SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4773
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6791
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6795
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6767
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7309
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.01, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7306
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7319
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7216
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6022
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5918
-- SVC(C=0.05, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4795
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7297
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7301
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7303
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7306
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7304
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7300
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7231
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6018
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5737
-- SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4813
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6774
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7301
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7296
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7272
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7245
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7227
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7159
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6631
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6005
-- SVC(C=0.5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4887
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7303
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7300
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7299
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7218
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7201
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7175
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7074
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6505
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5992
-- SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4815
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7301
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7251
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7197
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7112
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7114
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6835
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6530
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6072
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5840
-- SVC(C=5, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4841
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0001, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7294
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0005, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7194
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7178
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.005,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.7113
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.01,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6984
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.05,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6629
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6276
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.6056
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=1,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.5834
-- SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=10,
kernel='rbf', max_iter=-1, probability=True, random_state=None,
shrinking=True, tol=0.001, verbose=False)
-- AUC: 0.4914
== Best Model for SVC = SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False), AUC = 0.7332
Base Model ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7089
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6724
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6755
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7023
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6576
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6697
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7000
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6638
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6815
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.7061
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6837
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1, oob_score=False,
random_state=None, verbose=0)
-- AUC: 0.6803
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7017
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6804
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6735
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7054
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6789
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6748
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7120
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6757
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6809
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7116
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6881
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6784
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7111
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6815
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6801
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7085
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6837
-- ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6843
== Best Model for ExtraTreesClassifier = ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7120
Base Model LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
Param_grid #1/1 {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10], 'fit_intercept': [True, False]}
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l1',
random_state=None, tol=0.0001)
-- AUC: 0.5000
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6459
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.6459
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7160
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7162
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7178
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7186
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7175
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7180
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7174
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7182
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7151
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7156
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7151
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l1', random_state=None, tol=0.0001)
-- AUC: 0.7163
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7269
-- LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7269
-- LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7269
-- LogisticRegression(C=0.001, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7269
-- LogisticRegression(C=0.005, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7250
-- LogisticRegression(C=0.005, class_weight=None, dual=False,
fit_intercept=False, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- AUC: 0.7250
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7241
-- LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7241
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7200
-- LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7208
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7187
-- LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7194
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7158
-- LogisticRegression(C=0.5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7169
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7156
-- LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7169
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7154
-- LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7163
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7155
-- LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=False,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
-- AUC: 0.7165
== Best Model for LogisticRegression = LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001), AUC = 0.7269
Base Model RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'criterion': ['gini', 'entropy'], 'max_depth': [10, 20, None]}
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6962
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6849
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6945
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6970
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6926
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=25, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6934
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7052
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6992
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.6989
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7150
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7036
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=50, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7099
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7179
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7091
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7091
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7118
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7019
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7198
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7132
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7035
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7092
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7146
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7082
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7093
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7169
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7052
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7101
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7179
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=20, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7122
-- RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=400, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- AUC: 0.7119
== Best Model for RandomForestClassifier = RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0), AUC = 0.7198
Base Model AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=50, random_state=None)
Param_grid #1/1 {'n_estimators': [25, 50, 100, 200, 400], 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]}
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=25, random_state=None)
-- AUC: 0.6263
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=25, random_state=None)
-- AUC: 0.6508
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=25, random_state=None)
-- AUC: 0.6752
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=25, random_state=None)
-- AUC: 0.6985
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=25, random_state=None)
-- AUC: 0.7163
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=25, random_state=None)
-- AUC: 0.6922
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=25, random_state=None)
-- AUC: 0.6728
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=50, random_state=None)
-- AUC: 0.6309
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=50, random_state=None)
-- AUC: 0.6744
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=50, random_state=None)
-- AUC: 0.6940
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None)
-- AUC: 0.7178
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=50, random_state=None)
-- AUC: 0.7121
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=50, random_state=None)
-- AUC: 0.6752
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=50, random_state=None)
-- AUC: 0.6599
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=100, random_state=None)
-- AUC: 0.6476
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=100, random_state=None)
-- AUC: 0.6938
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=100, random_state=None)
-- AUC: 0.6988
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=100, random_state=None)
-- AUC: 0.7123
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=100, random_state=None)
-- AUC: 0.7054
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=100, random_state=None)
-- AUC: 0.6738
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=100, random_state=None)
-- AUC: 0.6676
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=200, random_state=None)
-- AUC: 0.6670
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=200, random_state=None)
-- AUC: 0.6982
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=200, random_state=None)
-- AUC: 0.7107
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=200, random_state=None)
-- AUC: 0.7058
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=200, random_state=None)
-- AUC: 0.6941
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=200, random_state=None)
-- AUC: 0.6637
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=200, random_state=None)
-- AUC: 0.6464
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.001, n_estimators=400, random_state=None)
-- AUC: 0.6932
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.005, n_estimators=400, random_state=None)
-- AUC: 0.7113
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.01, n_estimators=400, random_state=None)
-- AUC: 0.7160
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=400, random_state=None)
-- AUC: 0.6937
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.1, n_estimators=400, random_state=None)
-- AUC: 0.6829
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.5, n_estimators=400, random_state=None)
-- AUC: 0.6508
-- AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,
n_estimators=400, random_state=None)
-- AUC: 0.6391
== Best Model for AdaBoostClassifier = AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None), AUC = 0.7178
Done with hyperparameter selection
==========
LogisticRegression
-- best model: LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, penalty='l2',
random_state=None, tol=0.0001)
-- params: {'penalty': 'l2', 'C': 0.0001, 'fit_intercept': True}
-- Fold #5 test AUC for LogisticRegression: 0.7557
ExtraTreesClassifier
-- best model: ExtraTreesClassifier(bootstrap=False, compute_importances=None,
criterion='gini', max_depth=10, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=200, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 200, 'criterion': 'gini', 'max_depth': 10}
-- Fold #5 test AUC for ExtraTreesClassifier: 0.7467
RandomForestClassifier
-- best model: RandomForestClassifier(bootstrap=True, compute_importances=None,
criterion='entropy', max_depth=None, max_features='auto',
max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0)
-- params: {'n_estimators': 100, 'criterion': 'entropy', 'max_depth': None}
-- Fold #5 test AUC for RandomForestClassifier: 0.7114
KNeighborsClassifier
-- best model: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
n_neighbors=31, p=2, weights='uniform')
-- params: {'n_neighbors': 31, 'weights': 'uniform'}
-- Fold #5 test AUC for KNeighborsClassifier: 0.7700
AdaBoostClassifier
-- best model: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=0.05, n_estimators=50, random_state=None)
-- params: {'n_estimators': 50, 'learning_rate': 0.05}
-- Fold #5 test AUC for AdaBoostClassifier: 0.7418
SVC
-- best model: SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.05, kernel='rbf', max_iter=-1, probability=True,
random_state=None, shrinking=True, tol=0.001, verbose=False)
-- params: {'kernel': 'rbf', 'C': 0.0001, 'gamma': 0.05, 'probability': True}
-- Fold #5 test AUC for SVC: 0.7394
LogisticRegression
AUCs [0.72727272727272729, 0.73768939393939403, 0.81669372294372289, 0.75841147269718701, 0.75573883161512023]
mean AUC: 0.7592
median AUC: 0.7557
std AUC: 0.0310
ExtraTreesClassifier
AUCs [0.73457792207792205, 0.71293290043290047, 0.82886904761904756, 0.73000551571980143, 0.7466666666666667]
mean AUC: 0.7506
median AUC: 0.7346
std AUC: 0.0406
KNeighborsClassifier
AUCs [0.73518668831168821, 0.70907738095238093, 0.83285984848484851, 0.69615278543849968, 0.77003436426116834]
mean AUC: 0.7487
median AUC: 0.7352
std AUC: 0.0491
RandomForestClassifier
AUCs [0.73376623376623373, 0.72091450216450226, 0.82941017316017318, 0.73917539988968561, 0.71140893470790378]
mean AUC: 0.7469
median AUC: 0.7338
std AUC: 0.0424
AdaBoostClassifier
AUCs [0.68391504329004327, 0.74235660173160178, 0.80262445887445877, 0.74214009928295643, 0.74178694158075598]
mean AUC: 0.7426
median AUC: 0.7421
std AUC: 0.0375
SVC
AUCs [0.73457792207792205, 0.73795995670995673, 0.81872294372294385, 0.75110314396028677, 0.73938144329896915]
mean AUC: 0.7563
median AUC: 0.7394
std AUC: 0.0317
In [ ]:
import sklearn.cross_validation
print "Baseline", max(Y_Obliteration.mean(), 1-Y_Obliteration.mean())
for model in models:
print model
model.fit(X
np.mean(sklearn.cross_validation.cross_val_score(lr, X, Y_Obliteration, cv = 10))
print "LR Accuracy", lr_acc
lr_auc = np.mean(sklearn.cross_validation.cross_val_score(lr, X, Y_Obliteration.astype(bool), cv = 10, scoring='roc_auc'))
print "LR AUC", lr_auc
rf_acc = np.mean(sklearn.cross_validation.cross_val_score(rf, X, Y_Obliteration, cv = 10))
print "RF Accuracy", rf_acc
rf_auc = np.mean(sklearn.cross_validation.cross_val_score(rf, X, Y_Obliteration.astype(bool), cv = 10, scoring='roc_auc'))
print "RF AUC", rf_auc
In [13]:
print "Feature selection by L1-penalized Logistic Regression"
from collections import Counter
counts = Counter()
feature_list = list(df_in.columns)
for f in feature_list:
counts[f] = 0
for model_class in [sklearn.svm.LinearSVC, sklearn.linear_model.LogisticRegression]:
for fraction in [0.6, 0.7, 0.8]:
for fit_intercept in [False, True]:
for c in [.02, .04, .08]:
clf = model_class(penalty = 'l1', C = c, fit_intercept=fit_intercept, dual=False)
for curr_iter in xrange(100):
mask = np.random.rand(len(Y_Obliteration)) > fraction
clf.fit(X[mask], Y_Obliteration[mask])
good = np.abs(clf.coef_).ravel() > 0
pred = clf.predict(X[~mask])
baseline = Y_Obliteration[~mask].mean()
baseline = max(baseline, 1-baseline)
held_out_accuracy = np.mean(pred == Y_Obliteration[~mask])
print "%s: C = %0.4f, intercept=%s iter = %d, n_samples = %d/%d, baseline = %0.4f, acc = %0.4f" % (
model_class.__name__, c, fit_intercept, curr_iter, mask.sum(), len(mask), baseline, held_out_accuracy
)
if held_out_accuracy / baseline < 1.05:
print "-- skip"
continue
elif np.all(good):
print "-- all features chosen!"
continue
curr_features =[feature_list[i] for i,b in enumerate(good) if b]
print "--", curr_features
for f in curr_features:
counts[f] += 1
print counts
Feature selection by L1-penalized Logistic Regression
LinearSVC: C = 0.0200, intercept=False iter = 0, n_samples = 350/862, baseline = 0.5488, acc = 0.6660
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 1, n_samples = 354/862, baseline = 0.5531, acc = 0.6929
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 2, n_samples = 351/862, baseline = 0.5793, acc = 0.7045
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 3, n_samples = 354/862, baseline = 0.5630, acc = 0.7106
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 4, n_samples = 359/862, baseline = 0.5467, acc = 0.6819
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 5, n_samples = 338/862, baseline = 0.5496, acc = 0.7061
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 6, n_samples = 335/862, baseline = 0.5446, acc = 0.6964
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 7, n_samples = 322/862, baseline = 0.5519, acc = 0.6926
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 8, n_samples = 340/862, baseline = 0.5441, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=False iter = 9, n_samples = 340/862, baseline = 0.5326, acc = 0.6858
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 10, n_samples = 327/862, baseline = 0.5364, acc = 0.6991
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 11, n_samples = 344/862, baseline = 0.5521, acc = 0.6950
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 12, n_samples = 344/862, baseline = 0.5560, acc = 0.6757
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 13, n_samples = 351/862, baseline = 0.5479, acc = 0.6751
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 14, n_samples = 354/862, baseline = 0.5295, acc = 0.6713
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 15, n_samples = 336/862, baseline = 0.5551, acc = 0.6920
-- [u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 16, n_samples = 324/862, baseline = 0.5558, acc = 0.6859
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 17, n_samples = 337/862, baseline = 0.5695, acc = 0.7219
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 18, n_samples = 329/862, baseline = 0.5441, acc = 0.6604
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=False iter = 19, n_samples = 363/862, baseline = 0.5451, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 20, n_samples = 328/862, baseline = 0.5225, acc = 0.6610
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 21, n_samples = 360/862, baseline = 0.5837, acc = 0.6992
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 22, n_samples = 338/862, baseline = 0.5515, acc = 0.6603
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 23, n_samples = 332/862, baseline = 0.5396, acc = 0.6792
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 24, n_samples = 351/862, baseline = 0.5577, acc = 0.6791
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 25, n_samples = 343/862, baseline = 0.5530, acc = 0.6879
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 26, n_samples = 361/862, baseline = 0.5509, acc = 0.6647
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 27, n_samples = 329/862, baseline = 0.5235, acc = 0.6604
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 28, n_samples = 345/862, baseline = 0.5609, acc = 0.7041
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 29, n_samples = 337/862, baseline = 0.5219, acc = 0.6762
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 30, n_samples = 348/862, baseline = 0.5486, acc = 0.6654
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 31, n_samples = 342/862, baseline = 0.5404, acc = 0.6731
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 32, n_samples = 360/862, baseline = 0.5518, acc = 0.6892
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 33, n_samples = 357/862, baseline = 0.5267, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 34, n_samples = 337/862, baseline = 0.5486, acc = 0.6686
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 35, n_samples = 334/862, baseline = 0.5473, acc = 0.6799
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=False iter = 36, n_samples = 334/862, baseline = 0.5720, acc = 0.6856
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 37, n_samples = 343/862, baseline = 0.5626, acc = 0.6744
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 38, n_samples = 327/862, baseline = 0.5439, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 39, n_samples = 345/862, baseline = 0.5164, acc = 0.6770
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 40, n_samples = 335/862, baseline = 0.5484, acc = 0.6907
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 41, n_samples = 371/862, baseline = 0.5397, acc = 0.7088
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 42, n_samples = 351/862, baseline = 0.5714, acc = 0.7065
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 43, n_samples = 346/862, baseline = 0.5640, acc = 0.6938
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 44, n_samples = 355/862, baseline = 0.5523, acc = 0.6706
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 45, n_samples = 340/862, baseline = 0.5307, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 46, n_samples = 352/862, baseline = 0.5667, acc = 0.6863
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 47, n_samples = 366/862, baseline = 0.5423, acc = 0.6855
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 48, n_samples = 364/862, baseline = 0.5321, acc = 0.6727
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 49, n_samples = 343/862, baseline = 0.5703, acc = 0.7129
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 50, n_samples = 337/862, baseline = 0.5505, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 51, n_samples = 362/862, baseline = 0.5400, acc = 0.6740
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 52, n_samples = 353/862, baseline = 0.5521, acc = 0.6916
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 53, n_samples = 356/862, baseline = 0.5375, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 54, n_samples = 321/862, baseline = 0.5453, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 55, n_samples = 347/862, baseline = 0.5398, acc = 0.6893
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 56, n_samples = 349/862, baseline = 0.5634, acc = 0.6998
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 57, n_samples = 343/862, baseline = 0.5414, acc = 0.6802
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 58, n_samples = 330/862, baseline = 0.5395, acc = 0.6748
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 59, n_samples = 333/862, baseline = 0.5425, acc = 0.7013
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 60, n_samples = 372/862, baseline = 0.5551, acc = 0.7082
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 61, n_samples = 330/862, baseline = 0.5583, acc = 0.6992
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 62, n_samples = 328/862, baseline = 0.5637, acc = 0.6891
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 63, n_samples = 330/862, baseline = 0.5301, acc = 0.6917
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 64, n_samples = 347/862, baseline = 0.5612, acc = 0.6738
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 65, n_samples = 324/862, baseline = 0.5483, acc = 0.6933
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 66, n_samples = 325/862, baseline = 0.5754, acc = 0.6778
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 67, n_samples = 342/862, baseline = 0.5538, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 68, n_samples = 319/862, baseline = 0.5746, acc = 0.6851
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 69, n_samples = 356/862, baseline = 0.5494, acc = 0.6502
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 70, n_samples = 357/862, baseline = 0.5485, acc = 0.6693
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 71, n_samples = 324/862, baseline = 0.5651, acc = 0.6822
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 72, n_samples = 348/862, baseline = 0.5350, acc = 0.6907
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 73, n_samples = 337/862, baseline = 0.5295, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 74, n_samples = 333/862, baseline = 0.5463, acc = 0.6900
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 75, n_samples = 331/862, baseline = 0.5782, acc = 0.6761
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 76, n_samples = 360/862, baseline = 0.5578, acc = 0.6773
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 77, n_samples = 366/862, baseline = 0.5464, acc = 0.6653
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 78, n_samples = 348/862, baseline = 0.5564, acc = 0.7062
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 79, n_samples = 355/862, baseline = 0.5621, acc = 0.6864
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=False iter = 80, n_samples = 351/862, baseline = 0.5479, acc = 0.6614
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 81, n_samples = 346/862, baseline = 0.5446, acc = 0.6899
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 82, n_samples = 348/862, baseline = 0.5661, acc = 0.7062
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 83, n_samples = 360/862, baseline = 0.5677, acc = 0.6952
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 84, n_samples = 340/862, baseline = 0.5709, acc = 0.7011
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 85, n_samples = 361/862, baseline = 0.5449, acc = 0.6707
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 86, n_samples = 332/862, baseline = 0.5736, acc = 0.6887
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 87, n_samples = 357/862, baseline = 0.5644, acc = 0.6851
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 88, n_samples = 345/862, baseline = 0.5513, acc = 0.6925
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 89, n_samples = 330/862, baseline = 0.5865, acc = 0.6992
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 90, n_samples = 350/862, baseline = 0.5566, acc = 0.6680
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 91, n_samples = 332/862, baseline = 0.5717, acc = 0.7057
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 92, n_samples = 367/862, baseline = 0.5515, acc = 0.7091
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 93, n_samples = 329/862, baseline = 0.5647, acc = 0.6904
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 94, n_samples = 332/862, baseline = 0.5434, acc = 0.6925
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 95, n_samples = 342/862, baseline = 0.5404, acc = 0.6712
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 96, n_samples = 350/862, baseline = 0.5527, acc = 0.6875
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 97, n_samples = 338/862, baseline = 0.5553, acc = 0.6832
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 98, n_samples = 358/862, baseline = 0.5456, acc = 0.6905
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 99, n_samples = 355/862, baseline = 0.5483, acc = 0.7041
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 0, n_samples = 360/862, baseline = 0.5418, acc = 0.6952
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 1, n_samples = 346/862, baseline = 0.5252, acc = 0.6667
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 2, n_samples = 320/862, baseline = 0.5517, acc = 0.6956
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 3, n_samples = 324/862, baseline = 0.5595, acc = 0.7119
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 4, n_samples = 339/862, baseline = 0.5335, acc = 0.6941
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 5, n_samples = 344/862, baseline = 0.5541, acc = 0.6757
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 6, n_samples = 332/862, baseline = 0.5509, acc = 0.6811
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 7, n_samples = 323/862, baseline = 0.5492, acc = 0.7124
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 8, n_samples = 344/862, baseline = 0.5367, acc = 0.7027
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 9, n_samples = 345/862, baseline = 0.5706, acc = 0.7099
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 10, n_samples = 341/862, baseline = 0.5374, acc = 0.6814
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 11, n_samples = 334/862, baseline = 0.5436, acc = 0.7102
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 12, n_samples = 341/862, baseline = 0.5509, acc = 0.6910
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 13, n_samples = 359/862, baseline = 0.5666, acc = 0.7058
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 14, n_samples = 338/862, baseline = 0.5496, acc = 0.6851
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 15, n_samples = 335/862, baseline = 0.5503, acc = 0.6983
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 16, n_samples = 355/862, baseline = 0.5325, acc = 0.6785
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 17, n_samples = 345/862, baseline = 0.5397, acc = 0.6925
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 18, n_samples = 325/862, baseline = 0.5196, acc = 0.6741
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 19, n_samples = 345/862, baseline = 0.5474, acc = 0.7079
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 20, n_samples = 360/862, baseline = 0.5518, acc = 0.6912
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 21, n_samples = 362/862, baseline = 0.5480, acc = 0.6980
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 22, n_samples = 366/862, baseline = 0.5383, acc = 0.6875
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 23, n_samples = 353/862, baseline = 0.5422, acc = 0.6817
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 24, n_samples = 347/862, baseline = 0.5379, acc = 0.6777
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 25, n_samples = 342/862, baseline = 0.5712, acc = 0.7135
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 26, n_samples = 343/862, baseline = 0.5434, acc = 0.6975
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 27, n_samples = 354/862, baseline = 0.5394, acc = 0.6850
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 28, n_samples = 363/862, baseline = 0.5351, acc = 0.7094
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 29, n_samples = 372/862, baseline = 0.5673, acc = 0.6755
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 30, n_samples = 345/862, baseline = 0.5706, acc = 0.6925
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 31, n_samples = 353/862, baseline = 0.5344, acc = 0.6778
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 32, n_samples = 359/862, baseline = 0.5666, acc = 0.6640
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 33, n_samples = 320/862, baseline = 0.5535, acc = 0.6863
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 34, n_samples = 340/862, baseline = 0.5383, acc = 0.6935
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 35, n_samples = 352/862, baseline = 0.5431, acc = 0.6980
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 36, n_samples = 352/862, baseline = 0.5412, acc = 0.6922
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 37, n_samples = 334/862, baseline = 0.5455, acc = 0.6667
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 38, n_samples = 344/862, baseline = 0.5425, acc = 0.6776
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 39, n_samples = 343/862, baseline = 0.5279, acc = 0.7129
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 40, n_samples = 324/862, baseline = 0.5520, acc = 0.6896
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 41, n_samples = 339/862, baseline = 0.5621, acc = 0.6902
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 42, n_samples = 350/862, baseline = 0.5508, acc = 0.6582
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 43, n_samples = 338/862, baseline = 0.5439, acc = 0.7042
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 44, n_samples = 334/862, baseline = 0.5398, acc = 0.6989
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 45, n_samples = 353/862, baseline = 0.5363, acc = 0.6719
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 46, n_samples = 339/862, baseline = 0.5507, acc = 0.7208
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 47, n_samples = 343/862, baseline = 0.5626, acc = 0.6975
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 48, n_samples = 352/862, baseline = 0.5627, acc = 0.7020
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 49, n_samples = 340/862, baseline = 0.5785, acc = 0.6973
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 50, n_samples = 333/862, baseline = 0.5388, acc = 0.6975
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 51, n_samples = 350/862, baseline = 0.5449, acc = 0.7227
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 52, n_samples = 327/862, baseline = 0.5495, acc = 0.6841
-- [u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 53, n_samples = 353/862, baseline = 0.5658, acc = 0.6837
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 54, n_samples = 315/862, baseline = 0.5612, acc = 0.7020
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 55, n_samples = 334/862, baseline = 0.5777, acc = 0.6894
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 56, n_samples = 333/862, baseline = 0.5709, acc = 0.6919
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 57, n_samples = 347/862, baseline = 0.5301, acc = 0.6971
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 58, n_samples = 339/862, baseline = 0.5793, acc = 0.6922
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 59, n_samples = 354/862, baseline = 0.5551, acc = 0.6732
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 60, n_samples = 354/862, baseline = 0.5610, acc = 0.6870
-- [u'Sex', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 61, n_samples = 370/862, baseline = 0.5386, acc = 0.7256
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 62, n_samples = 355/862, baseline = 0.5661, acc = 0.6824
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 63, n_samples = 364/862, baseline = 0.5542, acc = 0.6647
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 64, n_samples = 335/862, baseline = 0.5427, acc = 0.6983
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 65, n_samples = 331/862, baseline = 0.5574, acc = 0.6968
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 66, n_samples = 341/862, baseline = 0.5413, acc = 0.6891
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 67, n_samples = 345/862, baseline = 0.5609, acc = 0.6983
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 68, n_samples = 351/862, baseline = 0.5538, acc = 0.6947
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 69, n_samples = 346/862, baseline = 0.5465, acc = 0.6899
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 70, n_samples = 326/862, baseline = 0.5485, acc = 0.6996
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 71, n_samples = 360/862, baseline = 0.5478, acc = 0.6833
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 72, n_samples = 351/862, baseline = 0.5519, acc = 0.6888
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 73, n_samples = 380/862, baseline = 0.5664, acc = 0.6971
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 74, n_samples = 347/862, baseline = 0.5495, acc = 0.6835
-- ['Age', u'SM', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 75, n_samples = 331/862, baseline = 0.5461, acc = 0.6874
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 76, n_samples = 351/862, baseline = 0.5421, acc = 0.6830
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 77, n_samples = 334/862, baseline = 0.5625, acc = 0.7027
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 78, n_samples = 354/862, baseline = 0.5354, acc = 0.6831
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 79, n_samples = 350/862, baseline = 0.5605, acc = 0.6758
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 80, n_samples = 358/862, baseline = 0.5456, acc = 0.7103
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 81, n_samples = 335/862, baseline = 0.5503, acc = 0.7173
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 82, n_samples = 346/862, baseline = 0.5446, acc = 0.7093
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 83, n_samples = 365/862, baseline = 0.5433, acc = 0.6901
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 84, n_samples = 373/862, baseline = 0.5440, acc = 0.6892
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 85, n_samples = 367/862, baseline = 0.5212, acc = 0.6626
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 86, n_samples = 336/862, baseline = 0.5513, acc = 0.7281
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 87, n_samples = 352/862, baseline = 0.5412, acc = 0.6941
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 88, n_samples = 345/862, baseline = 0.5706, acc = 0.6789
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 89, n_samples = 370/862, baseline = 0.5346, acc = 0.6911
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 90, n_samples = 365/862, baseline = 0.5553, acc = 0.6781
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 91, n_samples = 340/862, baseline = 0.5709, acc = 0.7126
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 92, n_samples = 348/862, baseline = 0.5661, acc = 0.6965
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 93, n_samples = 358/862, baseline = 0.5675, acc = 0.7123
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 94, n_samples = 357/862, baseline = 0.5347, acc = 0.6693
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 95, n_samples = 359/862, baseline = 0.5606, acc = 0.6879
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 96, n_samples = 352/862, baseline = 0.5333, acc = 0.6882
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 97, n_samples = 347/862, baseline = 0.5845, acc = 0.6913
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 98, n_samples = 372/862, baseline = 0.5286, acc = 0.6796
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 99, n_samples = 332/862, baseline = 0.5736, acc = 0.6755
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 0, n_samples = 357/862, baseline = 0.5624, acc = 0.6891
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 1, n_samples = 341/862, baseline = 0.5413, acc = 0.6987
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 2, n_samples = 344/862, baseline = 0.5483, acc = 0.6892
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 3, n_samples = 351/862, baseline = 0.5323, acc = 0.6869
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 4, n_samples = 347/862, baseline = 0.5689, acc = 0.7010
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 5, n_samples = 360/862, baseline = 0.5717, acc = 0.6932
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 6, n_samples = 342/862, baseline = 0.5481, acc = 0.6885
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 7, n_samples = 372/862, baseline = 0.5551, acc = 0.7041
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 8, n_samples = 325/862, baseline = 0.5605, acc = 0.6797
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 9, n_samples = 354/862, baseline = 0.5531, acc = 0.7146
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 10, n_samples = 354/862, baseline = 0.5118, acc = 0.6831
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 11, n_samples = 352/862, baseline = 0.5333, acc = 0.6882
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 12, n_samples = 348/862, baseline = 0.5389, acc = 0.7160
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 13, n_samples = 338/862, baseline = 0.5611, acc = 0.7042
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 14, n_samples = 350/862, baseline = 0.5430, acc = 0.6953
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 15, n_samples = 358/862, baseline = 0.5853, acc = 0.7183
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 16, n_samples = 348/862, baseline = 0.5253, acc = 0.6751
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 17, n_samples = 342/862, baseline = 0.5577, acc = 0.7038
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 18, n_samples = 331/862, baseline = 0.5537, acc = 0.7043
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 19, n_samples = 331/862, baseline = 0.5443, acc = 0.6968
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 20, n_samples = 346/862, baseline = 0.5620, acc = 0.7093
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 21, n_samples = 319/862, baseline = 0.5709, acc = 0.6832
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 22, n_samples = 328/862, baseline = 0.5524, acc = 0.6742
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 23, n_samples = 340/862, baseline = 0.5402, acc = 0.6954
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 24, n_samples = 354/862, baseline = 0.5295, acc = 0.6634
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 25, n_samples = 320/862, baseline = 0.5572, acc = 0.6642
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 26, n_samples = 348/862, baseline = 0.5272, acc = 0.6965
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 27, n_samples = 364/862, baseline = 0.5602, acc = 0.6908
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 28, n_samples = 351/862, baseline = 0.5342, acc = 0.6869
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 29, n_samples = 329/862, baseline = 0.5666, acc = 0.7186
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 30, n_samples = 352/862, baseline = 0.5412, acc = 0.6784
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 31, n_samples = 360/862, baseline = 0.5538, acc = 0.7112
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 32, n_samples = 348/862, baseline = 0.5623, acc = 0.6965
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 33, n_samples = 357/862, baseline = 0.5267, acc = 0.6832
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 34, n_samples = 343/862, baseline = 0.5665, acc = 0.7187
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 35, n_samples = 387/862, baseline = 0.5263, acc = 0.6968
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 36, n_samples = 361/862, baseline = 0.5329, acc = 0.6687
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 37, n_samples = 340/862, baseline = 0.5441, acc = 0.7280
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 38, n_samples = 351/862, baseline = 0.5597, acc = 0.7065
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 39, n_samples = 335/862, baseline = 0.5598, acc = 0.6983
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 40, n_samples = 356/862, baseline = 0.5494, acc = 0.7095
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 41, n_samples = 355/862, baseline = 0.5700, acc = 0.7140
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 42, n_samples = 352/862, baseline = 0.5588, acc = 0.7098
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 43, n_samples = 347/862, baseline = 0.5689, acc = 0.6680
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 44, n_samples = 347/862, baseline = 0.5417, acc = 0.6990
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 45, n_samples = 353/862, baseline = 0.5560, acc = 0.7014
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 46, n_samples = 359/862, baseline = 0.5507, acc = 0.7018
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 47, n_samples = 348/862, baseline = 0.5311, acc = 0.6907
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 48, n_samples = 341/862, baseline = 0.5355, acc = 0.6756
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 49, n_samples = 341/862, baseline = 0.5605, acc = 0.7102
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 50, n_samples = 346/862, baseline = 0.5620, acc = 0.7016
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 51, n_samples = 339/862, baseline = 0.5430, acc = 0.6979
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 52, n_samples = 334/862, baseline = 0.5720, acc = 0.6648
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 53, n_samples = 334/862, baseline = 0.5682, acc = 0.7140
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 54, n_samples = 353/862, baseline = 0.5540, acc = 0.6857
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 55, n_samples = 338/862, baseline = 0.5630, acc = 0.7023
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 56, n_samples = 359/862, baseline = 0.5507, acc = 0.6839
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 57, n_samples = 334/862, baseline = 0.5663, acc = 0.6932
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 58, n_samples = 338/862, baseline = 0.5821, acc = 0.7042
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 59, n_samples = 341/862, baseline = 0.5509, acc = 0.6948
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 60, n_samples = 346/862, baseline = 0.5523, acc = 0.7035
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 61, n_samples = 341/862, baseline = 0.5413, acc = 0.6737
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 62, n_samples = 326/862, baseline = 0.5205, acc = 0.6884
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 63, n_samples = 347/862, baseline = 0.5495, acc = 0.7068
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 64, n_samples = 360/862, baseline = 0.5458, acc = 0.6873
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 65, n_samples = 335/862, baseline = 0.5541, acc = 0.6850
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 66, n_samples = 333/862, baseline = 0.5463, acc = 0.6786
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 67, n_samples = 341/862, baseline = 0.5605, acc = 0.6929
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 68, n_samples = 327/862, baseline = 0.5477, acc = 0.7121
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 69, n_samples = 348/862, baseline = 0.5467, acc = 0.7004
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 70, n_samples = 348/862, baseline = 0.5467, acc = 0.6887
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 71, n_samples = 356/862, baseline = 0.5553, acc = 0.7055
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 72, n_samples = 322/862, baseline = 0.5593, acc = 0.7093
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 73, n_samples = 357/862, baseline = 0.5525, acc = 0.7109
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 74, n_samples = 303/862, baseline = 0.5331, acc = 0.7030
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 75, n_samples = 332/862, baseline = 0.5340, acc = 0.6774
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 76, n_samples = 353/862, baseline = 0.5560, acc = 0.6837
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 77, n_samples = 344/862, baseline = 0.5579, acc = 0.7143
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 78, n_samples = 369/862, baseline = 0.5700, acc = 0.7039
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 79, n_samples = 354/862, baseline = 0.5591, acc = 0.6772
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 80, n_samples = 322/862, baseline = 0.5370, acc = 0.6815
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 81, n_samples = 337/862, baseline = 0.5524, acc = 0.6857
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 82, n_samples = 322/862, baseline = 0.5574, acc = 0.7074
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 83, n_samples = 340/862, baseline = 0.5632, acc = 0.7011
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 84, n_samples = 348/862, baseline = 0.5739, acc = 0.7082
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 85, n_samples = 338/862, baseline = 0.5363, acc = 0.6908
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 86, n_samples = 375/862, baseline = 0.5298, acc = 0.6982
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 87, n_samples = 358/862, baseline = 0.5536, acc = 0.7143
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 88, n_samples = 347/862, baseline = 0.5417, acc = 0.6990
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 89, n_samples = 337/862, baseline = 0.5524, acc = 0.7219
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 90, n_samples = 336/862, baseline = 0.5856, acc = 0.6692
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 91, n_samples = 333/862, baseline = 0.5331, acc = 0.6767
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 92, n_samples = 349/862, baseline = 0.5750, acc = 0.7115
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 93, n_samples = 358/862, baseline = 0.5575, acc = 0.7103
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 94, n_samples = 339/862, baseline = 0.5315, acc = 0.6883
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 95, n_samples = 349/862, baseline = 0.5283, acc = 0.6745
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 96, n_samples = 341/862, baseline = 0.5393, acc = 0.6967
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 97, n_samples = 326/862, baseline = 0.5373, acc = 0.6698
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 98, n_samples = 328/862, baseline = 0.5468, acc = 0.6929
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 99, n_samples = 341/862, baseline = 0.5547, acc = 0.6948
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=True iter = 0, n_samples = 370/862, baseline = 0.5589, acc = 0.6911
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 1, n_samples = 340/862, baseline = 0.5402, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 2, n_samples = 335/862, baseline = 0.5522, acc = 0.6926
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 3, n_samples = 354/862, baseline = 0.5374, acc = 0.6811
-- [u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 4, n_samples = 350/862, baseline = 0.5469, acc = 0.6680
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 5, n_samples = 313/862, baseline = 0.5683, acc = 0.6831
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 6, n_samples = 346/862, baseline = 0.5368, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 7, n_samples = 326/862, baseline = 0.5653, acc = 0.6586
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 8, n_samples = 328/862, baseline = 0.5524, acc = 0.6966
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 9, n_samples = 358/862, baseline = 0.5377, acc = 0.6667
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=True iter = 10, n_samples = 337/862, baseline = 0.5467, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 11, n_samples = 360/862, baseline = 0.5398, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 12, n_samples = 335/862, baseline = 0.5104, acc = 0.6584
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 13, n_samples = 345/862, baseline = 0.5648, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 14, n_samples = 298/862, baseline = 0.5567, acc = 0.7128
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 15, n_samples = 321/862, baseline = 0.5619, acc = 0.6858
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 16, n_samples = 319/862, baseline = 0.5562, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 17, n_samples = 353/862, baseline = 0.5540, acc = 0.6719
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 18, n_samples = 331/862, baseline = 0.5612, acc = 0.6874
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=True iter = 19, n_samples = 331/862, baseline = 0.5650, acc = 0.6874
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 20, n_samples = 355/862, baseline = 0.5424, acc = 0.6963
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 21, n_samples = 334/862, baseline = 0.5398, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 22, n_samples = 350/862, baseline = 0.5684, acc = 0.6758
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 23, n_samples = 347/862, baseline = 0.5709, acc = 0.7087
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 24, n_samples = 350/862, baseline = 0.5625, acc = 0.7012
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 25, n_samples = 332/862, baseline = 0.5377, acc = 0.6830
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 26, n_samples = 359/862, baseline = 0.5388, acc = 0.6819
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 27, n_samples = 369/862, baseline = 0.5335, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 28, n_samples = 367/862, baseline = 0.5354, acc = 0.6909
-- [u'Sex', 'Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 29, n_samples = 313/862, baseline = 0.5592, acc = 0.7013
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 30, n_samples = 338/862, baseline = 0.5573, acc = 0.7214
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 31, n_samples = 341/862, baseline = 0.5374, acc = 0.6967
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 32, n_samples = 323/862, baseline = 0.5436, acc = 0.6920
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 33, n_samples = 364/862, baseline = 0.5683, acc = 0.6888
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 34, n_samples = 337/862, baseline = 0.5600, acc = 0.7143
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 35, n_samples = 335/862, baseline = 0.5332, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 36, n_samples = 332/862, baseline = 0.5283, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 37, n_samples = 355/862, baseline = 0.5582, acc = 0.6963
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 38, n_samples = 352/862, baseline = 0.5627, acc = 0.7098
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 39, n_samples = 342/862, baseline = 0.5404, acc = 0.6846
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 40, n_samples = 331/862, baseline = 0.5480, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 41, n_samples = 348/862, baseline = 0.5331, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 42, n_samples = 345/862, baseline = 0.5725, acc = 0.7002
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 43, n_samples = 351/862, baseline = 0.5695, acc = 0.6654
-- ['Age', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 44, n_samples = 359/862, baseline = 0.5249, acc = 0.6620
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 45, n_samples = 337/862, baseline = 0.5486, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 46, n_samples = 335/862, baseline = 0.5712, acc = 0.6812
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 47, n_samples = 340/862, baseline = 0.5326, acc = 0.6628
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 48, n_samples = 351/862, baseline = 0.5245, acc = 0.6967
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 49, n_samples = 340/862, baseline = 0.5536, acc = 0.6897
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 50, n_samples = 333/862, baseline = 0.5274, acc = 0.6711
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 51, n_samples = 359/862, baseline = 0.5249, acc = 0.6859
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 52, n_samples = 352/862, baseline = 0.5529, acc = 0.6608
-- [u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 53, n_samples = 367/862, baseline = 0.5616, acc = 0.6869
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 54, n_samples = 344/862, baseline = 0.5290, acc = 0.6757
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 55, n_samples = 319/862, baseline = 0.5488, acc = 0.6943
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 56, n_samples = 320/862, baseline = 0.5646, acc = 0.6827
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 57, n_samples = 322/862, baseline = 0.5611, acc = 0.6852
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 58, n_samples = 324/862, baseline = 0.5390, acc = 0.6877
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 59, n_samples = 335/862, baseline = 0.5598, acc = 0.6869
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 60, n_samples = 344/862, baseline = 0.5328, acc = 0.7027
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 61, n_samples = 347/862, baseline = 0.5786, acc = 0.6951
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 62, n_samples = 360/862, baseline = 0.5518, acc = 0.6932
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 63, n_samples = 340/862, baseline = 0.5479, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 64, n_samples = 355/862, baseline = 0.5365, acc = 0.7061
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 65, n_samples = 332/862, baseline = 0.5396, acc = 0.6925
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 66, n_samples = 357/862, baseline = 0.5584, acc = 0.7089
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 67, n_samples = 340/862, baseline = 0.5670, acc = 0.6762
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 68, n_samples = 325/862, baseline = 0.5549, acc = 0.6685
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 69, n_samples = 331/862, baseline = 0.5631, acc = 0.6930
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 70, n_samples = 368/862, baseline = 0.5445, acc = 0.6943
-- [u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 71, n_samples = 325/862, baseline = 0.5568, acc = 0.6741
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 72, n_samples = 346/862, baseline = 0.5659, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 73, n_samples = 367/862, baseline = 0.5515, acc = 0.7051
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 74, n_samples = 347/862, baseline = 0.5417, acc = 0.7010
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 75, n_samples = 367/862, baseline = 0.5657, acc = 0.7010
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 76, n_samples = 355/862, baseline = 0.5345, acc = 0.6844
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 77, n_samples = 363/862, baseline = 0.5551, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 78, n_samples = 337/862, baseline = 0.5505, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 79, n_samples = 326/862, baseline = 0.5765, acc = 0.6922
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 80, n_samples = 367/862, baseline = 0.5374, acc = 0.7010
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 81, n_samples = 331/862, baseline = 0.5631, acc = 0.6460
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 82, n_samples = 341/862, baseline = 0.5547, acc = 0.7044
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 83, n_samples = 349/862, baseline = 0.5653, acc = 0.6784
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 84, n_samples = 354/862, baseline = 0.5630, acc = 0.6890
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 85, n_samples = 339/862, baseline = 0.5468, acc = 0.7055
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 86, n_samples = 330/862, baseline = 0.5282, acc = 0.6729
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 87, n_samples = 339/862, baseline = 0.5430, acc = 0.6539
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 88, n_samples = 343/862, baseline = 0.5376, acc = 0.6802
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 89, n_samples = 369/862, baseline = 0.5477, acc = 0.6876
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 90, n_samples = 362/862, baseline = 0.5700, acc = 0.6720
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 91, n_samples = 358/862, baseline = 0.5218, acc = 0.6627
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 92, n_samples = 339/862, baseline = 0.5564, acc = 0.7094
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 93, n_samples = 323/862, baseline = 0.5659, acc = 0.6994
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 94, n_samples = 351/862, baseline = 0.5656, acc = 0.7006
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 95, n_samples = 333/862, baseline = 0.5406, acc = 0.6767
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 96, n_samples = 342/862, baseline = 0.5462, acc = 0.6692
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 97, n_samples = 338/862, baseline = 0.5496, acc = 0.6775
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 98, n_samples = 342/862, baseline = 0.5558, acc = 0.6923
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 99, n_samples = 331/862, baseline = 0.5292, acc = 0.6836
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 0, n_samples = 360/862, baseline = 0.5299, acc = 0.6793
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 1, n_samples = 363/862, baseline = 0.5531, acc = 0.7014
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 2, n_samples = 335/862, baseline = 0.5541, acc = 0.6736
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 3, n_samples = 352/862, baseline = 0.5569, acc = 0.6843
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 4, n_samples = 342/862, baseline = 0.5481, acc = 0.6904
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 5, n_samples = 317/862, baseline = 0.5523, acc = 0.6807
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 6, n_samples = 346/862, baseline = 0.5484, acc = 0.6977
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 7, n_samples = 350/862, baseline = 0.5371, acc = 0.6699
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 8, n_samples = 343/862, baseline = 0.5549, acc = 0.6763
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 9, n_samples = 372/862, baseline = 0.5469, acc = 0.6878
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 10, n_samples = 314/862, baseline = 0.5547, acc = 0.7135
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 11, n_samples = 336/862, baseline = 0.5399, acc = 0.7053
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 12, n_samples = 355/862, baseline = 0.5562, acc = 0.6884
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 13, n_samples = 336/862, baseline = 0.5418, acc = 0.6920
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 14, n_samples = 352/862, baseline = 0.5451, acc = 0.6843
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 15, n_samples = 353/862, baseline = 0.5678, acc = 0.7073
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 16, n_samples = 354/862, baseline = 0.5630, acc = 0.7047
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 17, n_samples = 323/862, baseline = 0.5139, acc = 0.6605
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 18, n_samples = 351/862, baseline = 0.5597, acc = 0.7104
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 19, n_samples = 346/862, baseline = 0.5581, acc = 0.6977
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 20, n_samples = 335/862, baseline = 0.5560, acc = 0.6812
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 21, n_samples = 368/862, baseline = 0.5243, acc = 0.6802
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 22, n_samples = 332/862, baseline = 0.5472, acc = 0.7000
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 23, n_samples = 352/862, baseline = 0.5784, acc = 0.7098
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 24, n_samples = 352/862, baseline = 0.5510, acc = 0.6804
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 25, n_samples = 341/862, baseline = 0.5701, acc = 0.7044
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 26, n_samples = 346/862, baseline = 0.5543, acc = 0.6938
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 27, n_samples = 336/862, baseline = 0.5779, acc = 0.7015
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 28, n_samples = 367/862, baseline = 0.5475, acc = 0.6869
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 29, n_samples = 344/862, baseline = 0.5618, acc = 0.6776
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 30, n_samples = 317/862, baseline = 0.5578, acc = 0.7046
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 31, n_samples = 350/862, baseline = 0.5273, acc = 0.6680
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 32, n_samples = 331/862, baseline = 0.5348, acc = 0.6798
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 33, n_samples = 355/862, baseline = 0.5641, acc = 0.6864
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 34, n_samples = 328/862, baseline = 0.5487, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 35, n_samples = 341/862, baseline = 0.5451, acc = 0.7025
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 36, n_samples = 337/862, baseline = 0.5276, acc = 0.6914
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 37, n_samples = 364/862, baseline = 0.5582, acc = 0.7129
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 38, n_samples = 355/862, baseline = 0.5247, acc = 0.6805
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 39, n_samples = 348/862, baseline = 0.5603, acc = 0.6556
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 40, n_samples = 360/862, baseline = 0.5677, acc = 0.7032
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 41, n_samples = 332/862, baseline = 0.5472, acc = 0.6943
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 42, n_samples = 341/862, baseline = 0.5605, acc = 0.6948
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 43, n_samples = 337/862, baseline = 0.5467, acc = 0.6914
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 44, n_samples = 365/862, baseline = 0.5473, acc = 0.6901
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 45, n_samples = 358/862, baseline = 0.5575, acc = 0.7063
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 46, n_samples = 318/862, baseline = 0.5515, acc = 0.7022
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 47, n_samples = 357/862, baseline = 0.5446, acc = 0.7010
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 48, n_samples = 342/862, baseline = 0.5712, acc = 0.7038
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 49, n_samples = 344/862, baseline = 0.5463, acc = 0.6795
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 50, n_samples = 359/862, baseline = 0.5447, acc = 0.6978
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 51, n_samples = 342/862, baseline = 0.5615, acc = 0.6750
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 52, n_samples = 338/862, baseline = 0.5553, acc = 0.6870
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 53, n_samples = 331/862, baseline = 0.5518, acc = 0.7062
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 54, n_samples = 342/862, baseline = 0.5250, acc = 0.6846
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 55, n_samples = 350/862, baseline = 0.5566, acc = 0.6992
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 56, n_samples = 343/862, baseline = 0.5279, acc = 0.6898
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 57, n_samples = 360/862, baseline = 0.5677, acc = 0.6892
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 58, n_samples = 327/862, baseline = 0.5551, acc = 0.6991
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 59, n_samples = 338/862, baseline = 0.5515, acc = 0.6832
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 60, n_samples = 356/862, baseline = 0.5593, acc = 0.7016
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 61, n_samples = 352/862, baseline = 0.5608, acc = 0.7176
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 62, n_samples = 356/862, baseline = 0.5356, acc = 0.6957
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 63, n_samples = 336/862, baseline = 0.5646, acc = 0.7034
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 64, n_samples = 335/862, baseline = 0.5522, acc = 0.7040
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 65, n_samples = 358/862, baseline = 0.5575, acc = 0.7004
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 66, n_samples = 346/862, baseline = 0.5349, acc = 0.6977
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 67, n_samples = 319/862, baseline = 0.5562, acc = 0.6777
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 68, n_samples = 338/862, baseline = 0.5363, acc = 0.6908
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 69, n_samples = 351/862, baseline = 0.5636, acc = 0.7025
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 70, n_samples = 345/862, baseline = 0.5532, acc = 0.7060
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 71, n_samples = 363/862, baseline = 0.5731, acc = 0.7054
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 72, n_samples = 342/862, baseline = 0.5288, acc = 0.6692
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 73, n_samples = 355/862, baseline = 0.5621, acc = 0.7022
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 74, n_samples = 340/862, baseline = 0.5364, acc = 0.6858
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 75, n_samples = 328/862, baseline = 0.5693, acc = 0.6985
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 76, n_samples = 361/862, baseline = 0.5369, acc = 0.6766
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 77, n_samples = 331/862, baseline = 0.5518, acc = 0.6723
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 78, n_samples = 349/862, baseline = 0.5419, acc = 0.6998
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 79, n_samples = 352/862, baseline = 0.5451, acc = 0.6824
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 80, n_samples = 359/862, baseline = 0.5368, acc = 0.6879
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 81, n_samples = 336/862, baseline = 0.5361, acc = 0.6996
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 82, n_samples = 341/862, baseline = 0.5720, acc = 0.7198
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 83, n_samples = 331/862, baseline = 0.5424, acc = 0.6930
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 84, n_samples = 345/862, baseline = 0.5783, acc = 0.6905
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 85, n_samples = 349/862, baseline = 0.5575, acc = 0.7096
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 86, n_samples = 365/862, baseline = 0.5634, acc = 0.6620
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 87, n_samples = 315/862, baseline = 0.5521, acc = 0.7038
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 88, n_samples = 337/862, baseline = 0.5562, acc = 0.6990
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 89, n_samples = 360/862, baseline = 0.5279, acc = 0.7072
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 90, n_samples = 367/862, baseline = 0.5354, acc = 0.6869
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 91, n_samples = 335/862, baseline = 0.5427, acc = 0.6983
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 92, n_samples = 341/862, baseline = 0.5547, acc = 0.6814
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 93, n_samples = 348/862, baseline = 0.5739, acc = 0.6965
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 94, n_samples = 337/862, baseline = 0.5524, acc = 0.7162
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 95, n_samples = 357/862, baseline = 0.5564, acc = 0.6950
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 96, n_samples = 333/862, baseline = 0.5312, acc = 0.6767
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 97, n_samples = 323/862, baseline = 0.5584, acc = 0.6809
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 98, n_samples = 348/862, baseline = 0.5506, acc = 0.6965
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 99, n_samples = 368/862, baseline = 0.5607, acc = 0.6862
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 0, n_samples = 351/862, baseline = 0.5538, acc = 0.7104
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 1, n_samples = 372/862, baseline = 0.5694, acc = 0.7020
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 2, n_samples = 350/862, baseline = 0.5547, acc = 0.6953
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 3, n_samples = 319/862, baseline = 0.5433, acc = 0.6832
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 4, n_samples = 343/862, baseline = 0.5530, acc = 0.6917
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 5, n_samples = 349/862, baseline = 0.5146, acc = 0.6764
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 6, n_samples = 337/862, baseline = 0.5524, acc = 0.6971
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 7, n_samples = 341/862, baseline = 0.5662, acc = 0.6967
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 8, n_samples = 325/862, baseline = 0.5531, acc = 0.7076
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 9, n_samples = 346/862, baseline = 0.5581, acc = 0.7054
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 10, n_samples = 369/862, baseline = 0.5598, acc = 0.7241
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 11, n_samples = 351/862, baseline = 0.5538, acc = 0.6888
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 12, n_samples = 346/862, baseline = 0.5446, acc = 0.7112
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 13, n_samples = 344/862, baseline = 0.5251, acc = 0.6892
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 14, n_samples = 351/862, baseline = 0.5636, acc = 0.6869
-- [u'Sex', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 15, n_samples = 346/862, baseline = 0.5349, acc = 0.6977
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 16, n_samples = 360/862, baseline = 0.5558, acc = 0.7112
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 17, n_samples = 349/862, baseline = 0.5361, acc = 0.6901
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 18, n_samples = 355/862, baseline = 0.5523, acc = 0.6963
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 19, n_samples = 345/862, baseline = 0.5474, acc = 0.6867
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 20, n_samples = 342/862, baseline = 0.5365, acc = 0.6962
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 21, n_samples = 321/862, baseline = 0.5656, acc = 0.7098
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 22, n_samples = 345/862, baseline = 0.5358, acc = 0.6867
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 23, n_samples = 357/862, baseline = 0.5366, acc = 0.6871
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0800, intercept=True iter = 24, n_samples = 360/862, baseline = 0.5378, acc = 0.6853
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 25, n_samples = 352/862, baseline = 0.5667, acc = 0.6765
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 26, n_samples = 351/862, baseline = 0.5519, acc = 0.6751
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 27, n_samples = 338/862, baseline = 0.5592, acc = 0.6908
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 28, n_samples = 328/862, baseline = 0.5693, acc = 0.7191
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 29, n_samples = 359/862, baseline = 0.5507, acc = 0.6958
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 30, n_samples = 373/862, baseline = 0.5706, acc = 0.7301
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 31, n_samples = 363/862, baseline = 0.5731, acc = 0.6713
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 32, n_samples = 353/862, baseline = 0.5501, acc = 0.7092
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 33, n_samples = 342/862, baseline = 0.5500, acc = 0.6885
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 34, n_samples = 360/862, baseline = 0.5518, acc = 0.6813
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 35, n_samples = 334/862, baseline = 0.5417, acc = 0.6799
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 36, n_samples = 318/862, baseline = 0.5515, acc = 0.6857
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 37, n_samples = 366/862, baseline = 0.5685, acc = 0.6996
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 38, n_samples = 363/862, baseline = 0.5651, acc = 0.6894
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 39, n_samples = 352/862, baseline = 0.5471, acc = 0.6902
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 40, n_samples = 328/862, baseline = 0.5543, acc = 0.6985
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 41, n_samples = 345/862, baseline = 0.5435, acc = 0.7253
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 42, n_samples = 349/862, baseline = 0.5595, acc = 0.7193
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 43, n_samples = 364/862, baseline = 0.5582, acc = 0.6988
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 44, n_samples = 364/862, baseline = 0.5602, acc = 0.7249
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 45, n_samples = 327/862, baseline = 0.5589, acc = 0.6953
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 46, n_samples = 357/862, baseline = 0.5604, acc = 0.6911
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 47, n_samples = 318/862, baseline = 0.5588, acc = 0.6893
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 48, n_samples = 354/862, baseline = 0.5610, acc = 0.6516
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 49, n_samples = 379/862, baseline = 0.5569, acc = 0.7516
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 50, n_samples = 353/862, baseline = 0.5383, acc = 0.7033
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 51, n_samples = 355/862, baseline = 0.5464, acc = 0.6903
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 52, n_samples = 352/862, baseline = 0.5490, acc = 0.7059
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 53, n_samples = 364/862, baseline = 0.5402, acc = 0.7088
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 54, n_samples = 326/862, baseline = 0.5541, acc = 0.6940
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 55, n_samples = 350/862, baseline = 0.5547, acc = 0.6914
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 56, n_samples = 333/862, baseline = 0.5482, acc = 0.6975
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 57, n_samples = 339/862, baseline = 0.5392, acc = 0.6769
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 58, n_samples = 364/862, baseline = 0.5442, acc = 0.7088
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 59, n_samples = 334/862, baseline = 0.5682, acc = 0.7159
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 60, n_samples = 354/862, baseline = 0.5531, acc = 0.7008
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 61, n_samples = 333/862, baseline = 0.5558, acc = 0.7070
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 62, n_samples = 312/862, baseline = 0.5564, acc = 0.6909
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 63, n_samples = 374/862, baseline = 0.5594, acc = 0.7295
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 64, n_samples = 323/862, baseline = 0.5510, acc = 0.7143
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 65, n_samples = 357/862, baseline = 0.5545, acc = 0.6752
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 66, n_samples = 322/862, baseline = 0.5667, acc = 0.6944
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 67, n_samples = 334/862, baseline = 0.5568, acc = 0.7102
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 68, n_samples = 340/862, baseline = 0.5287, acc = 0.7184
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 69, n_samples = 312/862, baseline = 0.5491, acc = 0.6927
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 70, n_samples = 348/862, baseline = 0.5467, acc = 0.7062
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 71, n_samples = 374/862, baseline = 0.5533, acc = 0.7070
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 72, n_samples = 327/862, baseline = 0.5439, acc = 0.7009
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 73, n_samples = 347/862, baseline = 0.5515, acc = 0.6932
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 74, n_samples = 326/862, baseline = 0.5634, acc = 0.6754
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 75, n_samples = 349/862, baseline = 0.5673, acc = 0.6901
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 76, n_samples = 343/862, baseline = 0.5164, acc = 0.6917
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 77, n_samples = 356/862, baseline = 0.5613, acc = 0.7134
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 78, n_samples = 350/862, baseline = 0.5566, acc = 0.6992
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 79, n_samples = 363/862, baseline = 0.5651, acc = 0.6914
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 80, n_samples = 340/862, baseline = 0.5556, acc = 0.6992
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 81, n_samples = 332/862, baseline = 0.5415, acc = 0.6755
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 82, n_samples = 358/862, baseline = 0.5476, acc = 0.6905
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 83, n_samples = 331/862, baseline = 0.5593, acc = 0.6685
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 84, n_samples = 360/862, baseline = 0.5737, acc = 0.7032
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 85, n_samples = 326/862, baseline = 0.5709, acc = 0.6847
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 86, n_samples = 329/862, baseline = 0.5591, acc = 0.7223
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 87, n_samples = 359/862, baseline = 0.5447, acc = 0.7137
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 88, n_samples = 336/862, baseline = 0.5646, acc = 0.7053
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 89, n_samples = 354/862, baseline = 0.5453, acc = 0.6870
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 90, n_samples = 333/862, baseline = 0.5425, acc = 0.6919
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 91, n_samples = 320/862, baseline = 0.5351, acc = 0.6845
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 92, n_samples = 319/862, baseline = 0.5635, acc = 0.7017
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 93, n_samples = 354/862, baseline = 0.5551, acc = 0.7047
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 94, n_samples = 369/862, baseline = 0.5416, acc = 0.6897
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 95, n_samples = 341/862, baseline = 0.5528, acc = 0.6852
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 96, n_samples = 348/862, baseline = 0.5584, acc = 0.7276
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 97, n_samples = 334/862, baseline = 0.5568, acc = 0.6951
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 98, n_samples = 345/862, baseline = 0.5455, acc = 0.6615
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 99, n_samples = 345/862, baseline = 0.5377, acc = 0.7060
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=False iter = 0, n_samples = 262/862, baseline = 0.5317, acc = 0.6833
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 1, n_samples = 251/862, baseline = 0.5548, acc = 0.6743
-- [u'Max D', 'Volume', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 2, n_samples = 252/862, baseline = 0.5361, acc = 0.7016
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 3, n_samples = 248/862, baseline = 0.5358, acc = 0.6808
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 4, n_samples = 289/862, baseline = 0.5428, acc = 0.6649
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 5, n_samples = 276/862, baseline = 0.5614, acc = 0.6877
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 6, n_samples = 236/862, baseline = 0.5543, acc = 0.6917
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 7, n_samples = 227/862, baseline = 0.5606, acc = 0.6898
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 8, n_samples = 268/862, baseline = 0.5556, acc = 0.6582
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 9, n_samples = 261/862, baseline = 0.5591, acc = 0.6905
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 10, n_samples = 256/862, baseline = 0.5297, acc = 0.6700
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 11, n_samples = 264/862, baseline = 0.5619, acc = 0.6622
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 12, n_samples = 236/862, baseline = 0.5511, acc = 0.6917
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 13, n_samples = 271/862, baseline = 0.5313, acc = 0.6717
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 14, n_samples = 263/862, baseline = 0.5593, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 15, n_samples = 253/862, baseline = 0.5369, acc = 0.6634
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 16, n_samples = 286/862, baseline = 0.5451, acc = 0.6944
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 17, n_samples = 242/862, baseline = 0.5774, acc = 0.6613
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 18, n_samples = 249/862, baseline = 0.5840, acc = 0.6868
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 19, n_samples = 249/862, baseline = 0.5498, acc = 0.6933
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 20, n_samples = 266/862, baseline = 0.5252, acc = 0.6477
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 21, n_samples = 243/862, baseline = 0.5590, acc = 0.6979
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 22, n_samples = 252/862, baseline = 0.5541, acc = 0.6689
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 23, n_samples = 248/862, baseline = 0.5521, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 24, n_samples = 263/862, baseline = 0.5543, acc = 0.6878
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 25, n_samples = 259/862, baseline = 0.5556, acc = 0.6833
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 26, n_samples = 264/862, baseline = 0.5635, acc = 0.6856
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 27, n_samples = 249/862, baseline = 0.5710, acc = 0.7113
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 28, n_samples = 242/862, baseline = 0.5661, acc = 0.6790
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 29, n_samples = 249/862, baseline = 0.5644, acc = 0.6933
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 30, n_samples = 274/862, baseline = 0.5561, acc = 0.6565
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 31, n_samples = 269/862, baseline = 0.5582, acc = 0.6931
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 32, n_samples = 238/862, baseline = 0.5545, acc = 0.6651
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 33, n_samples = 248/862, baseline = 0.5391, acc = 0.7052
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 34, n_samples = 271/862, baseline = 0.5601, acc = 0.6650
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 35, n_samples = 234/862, baseline = 0.5717, acc = 0.7006
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 36, n_samples = 255/862, baseline = 0.5486, acc = 0.6969
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 37, n_samples = 258/862, baseline = 0.5546, acc = 0.7003
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 38, n_samples = 256/862, baseline = 0.5495, acc = 0.7063
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 39, n_samples = 280/862, baseline = 0.5464, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 40, n_samples = 267/862, baseline = 0.5529, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 41, n_samples = 236/862, baseline = 0.5623, acc = 0.6741
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 42, n_samples = 264/862, baseline = 0.5452, acc = 0.6773
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 43, n_samples = 252/862, baseline = 0.5541, acc = 0.6787
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 44, n_samples = 274/862, baseline = 0.5578, acc = 0.7092
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 45, n_samples = 261/862, baseline = 0.5474, acc = 0.6822
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 46, n_samples = 273/862, baseline = 0.5552, acc = 0.6723
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 47, n_samples = 268/862, baseline = 0.5522, acc = 0.6734
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 48, n_samples = 254/862, baseline = 0.5428, acc = 0.6826
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 49, n_samples = 263/862, baseline = 0.5559, acc = 0.6861
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 50, n_samples = 252/862, baseline = 0.5656, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 51, n_samples = 249/862, baseline = 0.5449, acc = 0.6721
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 52, n_samples = 270/862, baseline = 0.5507, acc = 0.6622
-- [u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 53, n_samples = 262/862, baseline = 0.5550, acc = 0.6667
-- [u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 54, n_samples = 258/862, baseline = 0.5563, acc = 0.6887
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 55, n_samples = 261/862, baseline = 0.5391, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 56, n_samples = 234/862, baseline = 0.5573, acc = 0.6879
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 57, n_samples = 270/862, baseline = 0.5591, acc = 0.6976
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 58, n_samples = 265/862, baseline = 0.5327, acc = 0.6600
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 59, n_samples = 244/862, baseline = 0.5728, acc = 0.6861
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 60, n_samples = 257/862, baseline = 0.5488, acc = 0.6926
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 61, n_samples = 268/862, baseline = 0.5623, acc = 0.6582
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 62, n_samples = 274/862, baseline = 0.5544, acc = 0.6956
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 63, n_samples = 256/862, baseline = 0.5462, acc = 0.6815
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 64, n_samples = 243/862, baseline = 0.5460, acc = 0.6898
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 65, n_samples = 237/862, baseline = 0.5552, acc = 0.6752
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 66, n_samples = 256/862, baseline = 0.5627, acc = 0.7129
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 67, n_samples = 252/862, baseline = 0.5344, acc = 0.6820
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 68, n_samples = 241/862, baseline = 0.5604, acc = 0.6908
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 69, n_samples = 248/862, baseline = 0.5603, acc = 0.6759
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 70, n_samples = 266/862, baseline = 0.5386, acc = 0.7013
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 71, n_samples = 270/862, baseline = 0.5473, acc = 0.6943
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 72, n_samples = 240/862, baseline = 0.5498, acc = 0.6785
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 73, n_samples = 251/862, baseline = 0.5516, acc = 0.6923
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 74, n_samples = 266/862, baseline = 0.5705, acc = 0.6695
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 75, n_samples = 253/862, baseline = 0.5517, acc = 0.6650
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 76, n_samples = 244/862, baseline = 0.5485, acc = 0.6796
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 77, n_samples = 252/862, baseline = 0.5443, acc = 0.6721
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 78, n_samples = 275/862, baseline = 0.5247, acc = 0.6627
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 79, n_samples = 249/862, baseline = 0.5514, acc = 0.6966
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 80, n_samples = 249/862, baseline = 0.5693, acc = 0.6688
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 81, n_samples = 290/862, baseline = 0.5577, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 82, n_samples = 255/862, baseline = 0.5305, acc = 0.6804
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 83, n_samples = 239/862, baseline = 0.5409, acc = 0.6709
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 84, n_samples = 254/862, baseline = 0.5609, acc = 0.6957
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 85, n_samples = 247/862, baseline = 0.5431, acc = 0.6797
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 86, n_samples = 252/862, baseline = 0.5508, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 87, n_samples = 259/862, baseline = 0.5506, acc = 0.6866
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 88, n_samples = 283/862, baseline = 0.5354, acc = 0.6805
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 89, n_samples = 249/862, baseline = 0.5302, acc = 0.6639
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 90, n_samples = 260/862, baseline = 0.5465, acc = 0.6761
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 91, n_samples = 264/862, baseline = 0.5401, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 92, n_samples = 246/862, baseline = 0.5390, acc = 0.6753
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 93, n_samples = 254/862, baseline = 0.5296, acc = 0.6579
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 94, n_samples = 269/862, baseline = 0.5464, acc = 0.6712
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 95, n_samples = 236/862, baseline = 0.5447, acc = 0.6709
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 96, n_samples = 284/862, baseline = 0.5606, acc = 0.6782
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 97, n_samples = 276/862, baseline = 0.5375, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 98, n_samples = 258/862, baseline = 0.5397, acc = 0.6871
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 99, n_samples = 273/862, baseline = 0.5823, acc = 0.6961
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 0, n_samples = 260/862, baseline = 0.5615, acc = 0.6860
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 1, n_samples = 250/862, baseline = 0.5458, acc = 0.6961
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 2, n_samples = 246/862, baseline = 0.5373, acc = 0.6964
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 3, n_samples = 244/862, baseline = 0.5485, acc = 0.6942
-- [u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 4, n_samples = 269/862, baseline = 0.5295, acc = 0.6594
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 5, n_samples = 241/862, baseline = 0.5443, acc = 0.6973
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 6, n_samples = 257/862, baseline = 0.5620, acc = 0.7025
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 7, n_samples = 266/862, baseline = 0.5403, acc = 0.6795
-- [u'Sex', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 8, n_samples = 266/862, baseline = 0.5587, acc = 0.6745
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 9, n_samples = 262/862, baseline = 0.5417, acc = 0.6783
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 10, n_samples = 248/862, baseline = 0.5586, acc = 0.6938
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 11, n_samples = 240/862, baseline = 0.5723, acc = 0.6897
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 12, n_samples = 263/862, baseline = 0.5543, acc = 0.7045
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 13, n_samples = 279/862, baseline = 0.5403, acc = 0.6930
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 14, n_samples = 259/862, baseline = 0.5638, acc = 0.7148
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 15, n_samples = 259/862, baseline = 0.5489, acc = 0.6849
-- ['Age', u'Max D', u'Embo', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 16, n_samples = 246/862, baseline = 0.5471, acc = 0.6867
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 17, n_samples = 253/862, baseline = 0.5616, acc = 0.6946
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 18, n_samples = 252/862, baseline = 0.5557, acc = 0.6754
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 19, n_samples = 275/862, baseline = 0.5468, acc = 0.6917
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 20, n_samples = 282/862, baseline = 0.5500, acc = 0.6948
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 21, n_samples = 262/862, baseline = 0.5550, acc = 0.7133
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 22, n_samples = 276/862, baseline = 0.5495, acc = 0.6997
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 23, n_samples = 255/862, baseline = 0.5486, acc = 0.7051
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 24, n_samples = 258/862, baseline = 0.5563, acc = 0.6821
-- ['Age', u'SM', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 25, n_samples = 255/862, baseline = 0.5470, acc = 0.6755
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 26, n_samples = 269/862, baseline = 0.5312, acc = 0.7066
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 27, n_samples = 259/862, baseline = 0.5522, acc = 0.6716
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 28, n_samples = 254/862, baseline = 0.5362, acc = 0.6842
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 29, n_samples = 272/862, baseline = 0.5475, acc = 0.6847
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 30, n_samples = 274/862, baseline = 0.5544, acc = 0.7007
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 31, n_samples = 269/862, baseline = 0.5396, acc = 0.6779
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 32, n_samples = 243/862, baseline = 0.5541, acc = 0.6882
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 33, n_samples = 259/862, baseline = 0.5572, acc = 0.6766
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 34, n_samples = 242/862, baseline = 0.5452, acc = 0.6806
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 35, n_samples = 261/862, baseline = 0.5474, acc = 0.7022
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 36, n_samples = 275/862, baseline = 0.5639, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 37, n_samples = 280/862, baseline = 0.5584, acc = 0.6838
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 38, n_samples = 263/862, baseline = 0.5159, acc = 0.6678
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 39, n_samples = 267/862, baseline = 0.5513, acc = 0.6924
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 40, n_samples = 262/862, baseline = 0.5350, acc = 0.6817
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 41, n_samples = 282/862, baseline = 0.5328, acc = 0.6862
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 42, n_samples = 242/862, baseline = 0.5403, acc = 0.6806
-- [u'SM', u'Max D', 'Volume', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 43, n_samples = 252/862, baseline = 0.5525, acc = 0.6934
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 44, n_samples = 239/862, baseline = 0.5634, acc = 0.7127
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 45, n_samples = 250/862, baseline = 0.5588, acc = 0.6781
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 46, n_samples = 253/862, baseline = 0.5468, acc = 0.7011
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 47, n_samples = 253/862, baseline = 0.5698, acc = 0.6897
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 48, n_samples = 273/862, baseline = 0.5399, acc = 0.6893
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 49, n_samples = 247/862, baseline = 0.5545, acc = 0.6943
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 50, n_samples = 270/862, baseline = 0.5524, acc = 0.6807
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 51, n_samples = 269/862, baseline = 0.5514, acc = 0.6678
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 52, n_samples = 262/862, baseline = 0.5233, acc = 0.6533
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 53, n_samples = 264/862, baseline = 0.5518, acc = 0.6856
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 54, n_samples = 238/862, baseline = 0.5625, acc = 0.7051
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 55, n_samples = 251/862, baseline = 0.5483, acc = 0.6890
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 56, n_samples = 267/862, baseline = 0.5731, acc = 0.6840
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 57, n_samples = 258/862, baseline = 0.5480, acc = 0.6755
-- ['Age', u'SM', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 58, n_samples = 262/862, baseline = 0.5400, acc = 0.6833
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 59, n_samples = 253/862, baseline = 0.5567, acc = 0.6847
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 60, n_samples = 260/862, baseline = 0.5432, acc = 0.6661
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 61, n_samples = 261/862, baseline = 0.5524, acc = 0.6855
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 62, n_samples = 263/862, baseline = 0.5509, acc = 0.7145
-- [u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 63, n_samples = 265/862, baseline = 0.5611, acc = 0.7085
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 64, n_samples = 269/862, baseline = 0.5379, acc = 0.6762
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 65, n_samples = 252/862, baseline = 0.5459, acc = 0.6984
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 66, n_samples = 255/862, baseline = 0.5470, acc = 0.6837
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 67, n_samples = 266/862, baseline = 0.5419, acc = 0.6913
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 68, n_samples = 262/862, baseline = 0.5683, acc = 0.7067
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 69, n_samples = 271/862, baseline = 0.5516, acc = 0.6904
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 70, n_samples = 272/862, baseline = 0.5475, acc = 0.6881
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 71, n_samples = 253/862, baseline = 0.5484, acc = 0.6962
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 72, n_samples = 248/862, baseline = 0.5489, acc = 0.6661
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 73, n_samples = 248/862, baseline = 0.5554, acc = 0.6906
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 74, n_samples = 247/862, baseline = 0.5724, acc = 0.6667
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 75, n_samples = 263/862, baseline = 0.5426, acc = 0.6828
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 76, n_samples = 255/862, baseline = 0.5552, acc = 0.6886
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 77, n_samples = 261/862, baseline = 0.5607, acc = 0.7188
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 78, n_samples = 268/862, baseline = 0.5556, acc = 0.6717
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 79, n_samples = 248/862, baseline = 0.5440, acc = 0.6792
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 80, n_samples = 236/862, baseline = 0.5447, acc = 0.6837
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 81, n_samples = 271/862, baseline = 0.5431, acc = 0.6836
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 82, n_samples = 268/862, baseline = 0.5539, acc = 0.6818
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 83, n_samples = 246/862, baseline = 0.5503, acc = 0.6753
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 84, n_samples = 262/862, baseline = 0.5367, acc = 0.6850
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 85, n_samples = 237/862, baseline = 0.5552, acc = 0.7024
-- [u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 86, n_samples = 262/862, baseline = 0.5767, acc = 0.6967
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 87, n_samples = 263/862, baseline = 0.5426, acc = 0.6962
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 88, n_samples = 261/862, baseline = 0.5458, acc = 0.6872
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 89, n_samples = 255/862, baseline = 0.5535, acc = 0.6853
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 90, n_samples = 255/862, baseline = 0.5618, acc = 0.6738
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 91, n_samples = 269/862, baseline = 0.5481, acc = 0.7167
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 92, n_samples = 279/862, baseline = 0.5386, acc = 0.7136
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 93, n_samples = 293/862, baseline = 0.5712, acc = 0.6942
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 94, n_samples = 264/862, baseline = 0.5318, acc = 0.6873
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 95, n_samples = 269/862, baseline = 0.5514, acc = 0.6847
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 96, n_samples = 261/862, baseline = 0.5275, acc = 0.6722
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 97, n_samples = 262/862, baseline = 0.5433, acc = 0.7000
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 98, n_samples = 264/862, baseline = 0.5585, acc = 0.6890
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 99, n_samples = 266/862, baseline = 0.5688, acc = 0.6711
-- ['Age', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 0, n_samples = 254/862, baseline = 0.5625, acc = 0.7072
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 1, n_samples = 270/862, baseline = 0.5625, acc = 0.6976
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 2, n_samples = 259/862, baseline = 0.5406, acc = 0.6949
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 3, n_samples = 261/862, baseline = 0.5474, acc = 0.6739
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 4, n_samples = 260/862, baseline = 0.5449, acc = 0.6827
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 5, n_samples = 262/862, baseline = 0.5583, acc = 0.6933
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 6, n_samples = 275/862, baseline = 0.5434, acc = 0.7121
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 7, n_samples = 269/862, baseline = 0.5531, acc = 0.6965
-- [u'Sex', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 8, n_samples = 253/862, baseline = 0.5764, acc = 0.6864
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 9, n_samples = 266/862, baseline = 0.5671, acc = 0.6913
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 10, n_samples = 258/862, baseline = 0.5712, acc = 0.7053
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 11, n_samples = 247/862, baseline = 0.5496, acc = 0.6976
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 12, n_samples = 284/862, baseline = 0.5571, acc = 0.7128
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 13, n_samples = 246/862, baseline = 0.5438, acc = 0.6640
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 14, n_samples = 254/862, baseline = 0.5543, acc = 0.6859
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 15, n_samples = 242/862, baseline = 0.5435, acc = 0.6855
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 16, n_samples = 254/862, baseline = 0.5444, acc = 0.6908
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 17, n_samples = 254/862, baseline = 0.5641, acc = 0.6908
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 18, n_samples = 246/862, baseline = 0.5568, acc = 0.7045
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 19, n_samples = 247/862, baseline = 0.5740, acc = 0.6618
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 20, n_samples = 264/862, baseline = 0.5468, acc = 0.7007
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 21, n_samples = 253/862, baseline = 0.5435, acc = 0.6831
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 22, n_samples = 267/862, baseline = 0.5765, acc = 0.7227
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 23, n_samples = 265/862, baseline = 0.5494, acc = 0.6750
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 24, n_samples = 263/862, baseline = 0.5743, acc = 0.7162
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 25, n_samples = 246/862, baseline = 0.5373, acc = 0.6851
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 26, n_samples = 259/862, baseline = 0.5340, acc = 0.6982
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 27, n_samples = 243/862, baseline = 0.5622, acc = 0.6947
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 28, n_samples = 271/862, baseline = 0.5668, acc = 0.6870
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 29, n_samples = 246/862, baseline = 0.5633, acc = 0.6688
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 30, n_samples = 275/862, baseline = 0.5571, acc = 0.7070
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 31, n_samples = 259/862, baseline = 0.5572, acc = 0.6468
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 32, n_samples = 269/862, baseline = 0.5430, acc = 0.6880
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 33, n_samples = 269/862, baseline = 0.5565, acc = 0.6965
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 34, n_samples = 253/862, baseline = 0.5501, acc = 0.6880
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 35, n_samples = 232/862, baseline = 0.5635, acc = 0.6857
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 36, n_samples = 259/862, baseline = 0.5539, acc = 0.6965
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 37, n_samples = 253/862, baseline = 0.5517, acc = 0.6732
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 38, n_samples = 256/862, baseline = 0.5429, acc = 0.6518
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 39, n_samples = 260/862, baseline = 0.5399, acc = 0.7060
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 40, n_samples = 261/862, baseline = 0.5657, acc = 0.6722
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 41, n_samples = 265/862, baseline = 0.5662, acc = 0.7136
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 42, n_samples = 269/862, baseline = 0.5413, acc = 0.6931
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 43, n_samples = 221/862, baseline = 0.5289, acc = 0.6646
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 44, n_samples = 243/862, baseline = 0.5590, acc = 0.7011
-- [u'Sex', 'Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 45, n_samples = 239/862, baseline = 0.5409, acc = 0.6854
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 46, n_samples = 262/862, baseline = 0.5450, acc = 0.6983
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 47, n_samples = 250/862, baseline = 0.5490, acc = 0.6928
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 48, n_samples = 250/862, baseline = 0.5637, acc = 0.7010
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 49, n_samples = 250/862, baseline = 0.5605, acc = 0.6846
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 50, n_samples = 250/862, baseline = 0.5588, acc = 0.7042
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 51, n_samples = 244/862, baseline = 0.5663, acc = 0.7087
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 52, n_samples = 253/862, baseline = 0.5419, acc = 0.6946
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 53, n_samples = 282/862, baseline = 0.5638, acc = 0.7000
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 54, n_samples = 267/862, baseline = 0.5395, acc = 0.6756
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 55, n_samples = 240/862, baseline = 0.5322, acc = 0.6833
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 56, n_samples = 243/862, baseline = 0.5751, acc = 0.7027
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 57, n_samples = 274/862, baseline = 0.5680, acc = 0.7126
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 58, n_samples = 268/862, baseline = 0.5421, acc = 0.7020
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 59, n_samples = 219/862, baseline = 0.5537, acc = 0.6905
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 60, n_samples = 275/862, baseline = 0.5503, acc = 0.7104
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 61, n_samples = 256/862, baseline = 0.5528, acc = 0.7030
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 62, n_samples = 253/862, baseline = 0.5747, acc = 0.6782
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 63, n_samples = 268/862, baseline = 0.5572, acc = 0.6987
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 64, n_samples = 257/862, baseline = 0.5554, acc = 0.7140
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 65, n_samples = 250/862, baseline = 0.5507, acc = 0.6748
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 66, n_samples = 271/862, baseline = 0.5584, acc = 0.7039
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 67, n_samples = 273/862, baseline = 0.5365, acc = 0.6910
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 68, n_samples = 273/862, baseline = 0.5603, acc = 0.6808
-- [u'Sex', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 69, n_samples = 262/862, baseline = 0.5633, acc = 0.6917
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 70, n_samples = 236/862, baseline = 0.5511, acc = 0.6869
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 71, n_samples = 250/862, baseline = 0.5539, acc = 0.6912
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 72, n_samples = 260/862, baseline = 0.5565, acc = 0.6844
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 73, n_samples = 243/862, baseline = 0.5493, acc = 0.6931
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 74, n_samples = 255/862, baseline = 0.5502, acc = 0.6919
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 75, n_samples = 263/862, baseline = 0.5492, acc = 0.6962
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 76, n_samples = 241/862, baseline = 0.5539, acc = 0.6812
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 77, n_samples = 260/862, baseline = 0.5432, acc = 0.6944
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 78, n_samples = 212/862, baseline = 0.5538, acc = 0.6785
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 79, n_samples = 256/862, baseline = 0.5578, acc = 0.6898
-- ['Age', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 80, n_samples = 260/862, baseline = 0.5449, acc = 0.6977
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 81, n_samples = 286/862, baseline = 0.5417, acc = 0.6788
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 82, n_samples = 249/862, baseline = 0.5498, acc = 0.6835
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 83, n_samples = 252/862, baseline = 0.5738, acc = 0.7066
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 84, n_samples = 264/862, baseline = 0.5452, acc = 0.7007
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 85, n_samples = 280/862, baseline = 0.5601, acc = 0.6718
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 86, n_samples = 257/862, baseline = 0.5421, acc = 0.6843
-- [u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 87, n_samples = 257/862, baseline = 0.5388, acc = 0.6793
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 88, n_samples = 252/862, baseline = 0.5607, acc = 0.7033
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 89, n_samples = 254/862, baseline = 0.5543, acc = 0.7039
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 90, n_samples = 269/862, baseline = 0.5683, acc = 0.7083
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 91, n_samples = 260/862, baseline = 0.5581, acc = 0.6728
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 92, n_samples = 240/862, baseline = 0.5514, acc = 0.6768
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 93, n_samples = 264/862, baseline = 0.5468, acc = 0.7040
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 94, n_samples = 254/862, baseline = 0.5493, acc = 0.6957
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 95, n_samples = 273/862, baseline = 0.5433, acc = 0.6791
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 96, n_samples = 256/862, baseline = 0.5446, acc = 0.6997
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 97, n_samples = 238/862, baseline = 0.5497, acc = 0.7083
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 98, n_samples = 264/862, baseline = 0.5552, acc = 0.6806
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 99, n_samples = 280/862, baseline = 0.5430, acc = 0.7010
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 0, n_samples = 277/862, baseline = 0.5470, acc = 0.6889
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 1, n_samples = 278/862, baseline = 0.5599, acc = 0.6610
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 2, n_samples = 266/862, baseline = 0.5537, acc = 0.6829
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 3, n_samples = 277/862, baseline = 0.5538, acc = 0.6923
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 4, n_samples = 255/862, baseline = 0.5568, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 5, n_samples = 251/862, baseline = 0.5565, acc = 0.6776
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 6, n_samples = 245/862, baseline = 0.5446, acc = 0.6888
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 7, n_samples = 260/862, baseline = 0.5532, acc = 0.6877
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 8, n_samples = 259/862, baseline = 0.5589, acc = 0.7032
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 9, n_samples = 262/862, baseline = 0.5667, acc = 0.6883
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 10, n_samples = 270/862, baseline = 0.5287, acc = 0.6689
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 11, n_samples = 257/862, baseline = 0.5521, acc = 0.6793
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 12, n_samples = 263/862, baseline = 0.5476, acc = 0.6628
-- ['Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 13, n_samples = 260/862, baseline = 0.5880, acc = 0.6827
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 14, n_samples = 254/862, baseline = 0.5230, acc = 0.6859
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 15, n_samples = 255/862, baseline = 0.5667, acc = 0.6886
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 16, n_samples = 282/862, baseline = 0.5552, acc = 0.6759
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 17, n_samples = 247/862, baseline = 0.5593, acc = 0.7008
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 18, n_samples = 251/862, baseline = 0.5401, acc = 0.6743
-- [u'SM', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 19, n_samples = 258/862, baseline = 0.5546, acc = 0.7020
-- [u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 20, n_samples = 257/862, baseline = 0.5388, acc = 0.6760
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 21, n_samples = 258/862, baseline = 0.5281, acc = 0.6556
-- ['Age', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 22, n_samples = 245/862, baseline = 0.5462, acc = 0.6629
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 23, n_samples = 263/862, baseline = 0.5593, acc = 0.6978
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 24, n_samples = 258/862, baseline = 0.5464, acc = 0.6854
-- [u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 25, n_samples = 245/862, baseline = 0.5559, acc = 0.6840
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 26, n_samples = 242/862, baseline = 0.5500, acc = 0.7048
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 27, n_samples = 256/862, baseline = 0.5644, acc = 0.6848
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 28, n_samples = 250/862, baseline = 0.5588, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 29, n_samples = 273/862, baseline = 0.5586, acc = 0.6910
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 30, n_samples = 268/862, baseline = 0.5505, acc = 0.6835
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 31, n_samples = 236/862, baseline = 0.5511, acc = 0.6709
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 32, n_samples = 250/862, baseline = 0.5294, acc = 0.6797
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 33, n_samples = 257/862, baseline = 0.5669, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 34, n_samples = 247/862, baseline = 0.5528, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 35, n_samples = 245/862, baseline = 0.5575, acc = 0.7034
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 36, n_samples = 256/862, baseline = 0.5677, acc = 0.7079
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 37, n_samples = 279/862, baseline = 0.5472, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 38, n_samples = 240/862, baseline = 0.5386, acc = 0.6720
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 39, n_samples = 259/862, baseline = 0.5705, acc = 0.6998
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 40, n_samples = 249/862, baseline = 0.5693, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 41, n_samples = 260/862, baseline = 0.5748, acc = 0.6910
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 42, n_samples = 245/862, baseline = 0.5511, acc = 0.6969
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 43, n_samples = 249/862, baseline = 0.5677, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 44, n_samples = 269/862, baseline = 0.5413, acc = 0.6863
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 45, n_samples = 229/862, baseline = 0.5482, acc = 0.6761
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 46, n_samples = 233/862, baseline = 0.5564, acc = 0.6725
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 47, n_samples = 280/862, baseline = 0.5515, acc = 0.6718
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 48, n_samples = 280/862, baseline = 0.5687, acc = 0.6821
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 49, n_samples = 252/862, baseline = 0.5590, acc = 0.6508
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 50, n_samples = 239/862, baseline = 0.5522, acc = 0.6581
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 51, n_samples = 253/862, baseline = 0.5567, acc = 0.6929
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 52, n_samples = 285/862, baseline = 0.5494, acc = 0.6898
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 53, n_samples = 263/862, baseline = 0.5543, acc = 0.6945
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 54, n_samples = 281/862, baseline = 0.5508, acc = 0.6919
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 55, n_samples = 271/862, baseline = 0.5465, acc = 0.7056
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 56, n_samples = 267/862, baseline = 0.5664, acc = 0.6958
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 57, n_samples = 251/862, baseline = 0.5434, acc = 0.6678
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 58, n_samples = 267/862, baseline = 0.5697, acc = 0.7076
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 59, n_samples = 231/862, baseline = 0.5499, acc = 0.6593
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 60, n_samples = 262/862, baseline = 0.5550, acc = 0.7000
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 61, n_samples = 303/862, baseline = 0.5438, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 62, n_samples = 263/862, baseline = 0.5459, acc = 0.6628
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 63, n_samples = 259/862, baseline = 0.5572, acc = 0.6866
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 64, n_samples = 253/862, baseline = 0.5534, acc = 0.6798
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 65, n_samples = 264/862, baseline = 0.5351, acc = 0.6839
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 66, n_samples = 274/862, baseline = 0.5408, acc = 0.6701
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 67, n_samples = 253/862, baseline = 0.5550, acc = 0.6864
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 68, n_samples = 246/862, baseline = 0.5536, acc = 0.6607
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 69, n_samples = 264/862, baseline = 0.5334, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 70, n_samples = 247/862, baseline = 0.5675, acc = 0.6423
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 71, n_samples = 252/862, baseline = 0.5672, acc = 0.6869
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 72, n_samples = 244/862, baseline = 0.5550, acc = 0.6893
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 73, n_samples = 269/862, baseline = 0.5464, acc = 0.6762
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 74, n_samples = 256/862, baseline = 0.5462, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 75, n_samples = 254/862, baseline = 0.5444, acc = 0.7007
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 76, n_samples = 231/862, baseline = 0.5610, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 77, n_samples = 263/862, baseline = 0.5543, acc = 0.6895
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 78, n_samples = 234/862, baseline = 0.5462, acc = 0.6815
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 79, n_samples = 238/862, baseline = 0.5465, acc = 0.6795
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 80, n_samples = 244/862, baseline = 0.5502, acc = 0.6909
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 81, n_samples = 253/862, baseline = 0.5567, acc = 0.7077
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 82, n_samples = 271/862, baseline = 0.5482, acc = 0.6937
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 83, n_samples = 261/862, baseline = 0.5607, acc = 0.6972
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 84, n_samples = 238/862, baseline = 0.5465, acc = 0.6939
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 85, n_samples = 276/862, baseline = 0.5512, acc = 0.6945
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 86, n_samples = 260/862, baseline = 0.5565, acc = 0.7027
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 87, n_samples = 254/862, baseline = 0.5461, acc = 0.6809
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 88, n_samples = 234/862, baseline = 0.5398, acc = 0.6640
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 89, n_samples = 247/862, baseline = 0.5545, acc = 0.6927
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 90, n_samples = 253/862, baseline = 0.5468, acc = 0.6716
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 91, n_samples = 234/862, baseline = 0.5494, acc = 0.6847
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 92, n_samples = 270/862, baseline = 0.5456, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 93, n_samples = 267/862, baseline = 0.5546, acc = 0.6874
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 94, n_samples = 249/862, baseline = 0.5612, acc = 0.6998
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 95, n_samples = 254/862, baseline = 0.5362, acc = 0.6743
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 96, n_samples = 252/862, baseline = 0.5311, acc = 0.6656
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 97, n_samples = 262/862, baseline = 0.5283, acc = 0.6700
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 98, n_samples = 273/862, baseline = 0.5789, acc = 0.6876
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 99, n_samples = 257/862, baseline = 0.5554, acc = 0.6793
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 0, n_samples = 292/862, baseline = 0.5684, acc = 0.7105
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 1, n_samples = 255/862, baseline = 0.5519, acc = 0.6853
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 2, n_samples = 273/862, baseline = 0.5569, acc = 0.6893
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 3, n_samples = 249/862, baseline = 0.5563, acc = 0.6884
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 4, n_samples = 251/862, baseline = 0.5532, acc = 0.7005
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 5, n_samples = 272/862, baseline = 0.5542, acc = 0.7102
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 6, n_samples = 280/862, baseline = 0.5430, acc = 0.6753
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 7, n_samples = 264/862, baseline = 0.5485, acc = 0.6839
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 8, n_samples = 267/862, baseline = 0.5529, acc = 0.7025
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 9, n_samples = 245/862, baseline = 0.5737, acc = 0.7050
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 10, n_samples = 242/862, baseline = 0.5387, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 11, n_samples = 261/862, baseline = 0.5591, acc = 0.6988
-- ['Age', u'Max D', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 12, n_samples = 257/862, baseline = 0.5471, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 13, n_samples = 252/862, baseline = 0.5574, acc = 0.6984
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 14, n_samples = 269/862, baseline = 0.5666, acc = 0.7066
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 15, n_samples = 249/862, baseline = 0.5302, acc = 0.6803
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 16, n_samples = 240/862, baseline = 0.5514, acc = 0.7026
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 17, n_samples = 253/862, baseline = 0.5517, acc = 0.6946
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 18, n_samples = 244/862, baseline = 0.5615, acc = 0.6942
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 19, n_samples = 269/862, baseline = 0.5464, acc = 0.6847
-- [u'Sex', 'Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 20, n_samples = 239/862, baseline = 0.5490, acc = 0.6870
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 21, n_samples = 272/862, baseline = 0.5424, acc = 0.6780
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 22, n_samples = 248/862, baseline = 0.5651, acc = 0.6987
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 23, n_samples = 279/862, baseline = 0.5369, acc = 0.6741
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 24, n_samples = 277/862, baseline = 0.5265, acc = 0.6650
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 25, n_samples = 283/862, baseline = 0.5630, acc = 0.6908
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 26, n_samples = 237/862, baseline = 0.5568, acc = 0.6784
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 27, n_samples = 234/862, baseline = 0.5525, acc = 0.6895
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 28, n_samples = 227/862, baseline = 0.5654, acc = 0.6866
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 29, n_samples = 290/862, baseline = 0.5402, acc = 0.7098
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 30, n_samples = 262/862, baseline = 0.5500, acc = 0.6933
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 31, n_samples = 249/862, baseline = 0.5498, acc = 0.6688
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 32, n_samples = 241/862, baseline = 0.5556, acc = 0.6779
-- [u'Sex', 'Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 33, n_samples = 255/862, baseline = 0.5437, acc = 0.7018
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 34, n_samples = 248/862, baseline = 0.5407, acc = 0.6808
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 35, n_samples = 250/862, baseline = 0.5637, acc = 0.6895
-- [u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 36, n_samples = 265/862, baseline = 0.5494, acc = 0.6750
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 37, n_samples = 273/862, baseline = 0.5297, acc = 0.6791
-- [u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 38, n_samples = 242/862, baseline = 0.5468, acc = 0.6984
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 39, n_samples = 246/862, baseline = 0.5422, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 40, n_samples = 258/862, baseline = 0.5629, acc = 0.6821
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 41, n_samples = 256/862, baseline = 0.5479, acc = 0.6733
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 42, n_samples = 266/862, baseline = 0.5688, acc = 0.6846
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 43, n_samples = 258/862, baseline = 0.5315, acc = 0.6838
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 44, n_samples = 245/862, baseline = 0.5478, acc = 0.6921
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 45, n_samples = 236/862, baseline = 0.5607, acc = 0.6965
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 46, n_samples = 250/862, baseline = 0.5490, acc = 0.6895
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 47, n_samples = 233/862, baseline = 0.5469, acc = 0.6836
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 48, n_samples = 260/862, baseline = 0.5781, acc = 0.6694
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 49, n_samples = 271/862, baseline = 0.5550, acc = 0.6768
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 50, n_samples = 251/862, baseline = 0.5352, acc = 0.6939
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 51, n_samples = 260/862, baseline = 0.5482, acc = 0.6910
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 52, n_samples = 265/862, baseline = 0.5578, acc = 0.6935
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 53, n_samples = 248/862, baseline = 0.5391, acc = 0.6857
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 54, n_samples = 276/862, baseline = 0.5563, acc = 0.6928
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 55, n_samples = 267/862, baseline = 0.5412, acc = 0.6723
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 56, n_samples = 250/862, baseline = 0.5408, acc = 0.6585
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 57, n_samples = 246/862, baseline = 0.5357, acc = 0.6802
-- ['Age', u'SM', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 58, n_samples = 257/862, baseline = 0.5504, acc = 0.7058
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 59, n_samples = 251/862, baseline = 0.5499, acc = 0.6907
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 60, n_samples = 253/862, baseline = 0.5632, acc = 0.6897
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 61, n_samples = 262/862, baseline = 0.5500, acc = 0.6833
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 62, n_samples = 264/862, baseline = 0.5552, acc = 0.6722
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 63, n_samples = 275/862, baseline = 0.5554, acc = 0.6951
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 64, n_samples = 265/862, baseline = 0.5578, acc = 0.7035
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 65, n_samples = 286/862, baseline = 0.5538, acc = 0.6667
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 66, n_samples = 278/862, baseline = 0.5565, acc = 0.6952
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 67, n_samples = 289/862, baseline = 0.5497, acc = 0.7068
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 68, n_samples = 239/862, baseline = 0.5506, acc = 0.7159
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 69, n_samples = 245/862, baseline = 0.5592, acc = 0.6499
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 70, n_samples = 277/862, baseline = 0.5487, acc = 0.6718
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 71, n_samples = 259/862, baseline = 0.5738, acc = 0.7098
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 72, n_samples = 276/862, baseline = 0.5461, acc = 0.6741
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 73, n_samples = 267/862, baseline = 0.5244, acc = 0.6723
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 74, n_samples = 244/862, baseline = 0.5485, acc = 0.6958
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 75, n_samples = 264/862, baseline = 0.5619, acc = 0.6806
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 76, n_samples = 257/862, baseline = 0.5620, acc = 0.6975
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 77, n_samples = 296/862, baseline = 0.5654, acc = 0.7014
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 78, n_samples = 244/862, baseline = 0.5502, acc = 0.6796
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 79, n_samples = 235/862, baseline = 0.5678, acc = 0.7289
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 80, n_samples = 241/862, baseline = 0.5443, acc = 0.6860
-- ['Age', u'Max D', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 81, n_samples = 239/862, baseline = 0.5602, acc = 0.6677
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 82, n_samples = 255/862, baseline = 0.5453, acc = 0.6919
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 83, n_samples = 267/862, baseline = 0.5529, acc = 0.6773
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 84, n_samples = 264/862, baseline = 0.5585, acc = 0.7191
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 85, n_samples = 244/862, baseline = 0.5421, acc = 0.6942
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 86, n_samples = 253/862, baseline = 0.5452, acc = 0.6683
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 87, n_samples = 263/862, baseline = 0.5626, acc = 0.6912
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 88, n_samples = 257/862, baseline = 0.5636, acc = 0.7025
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 89, n_samples = 265/862, baseline = 0.5427, acc = 0.6884
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 90, n_samples = 250/862, baseline = 0.5507, acc = 0.7157
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 91, n_samples = 262/862, baseline = 0.5567, acc = 0.6700
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 92, n_samples = 283/862, baseline = 0.5596, acc = 0.6753
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 93, n_samples = 249/862, baseline = 0.5334, acc = 0.6819
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 94, n_samples = 263/862, baseline = 0.5693, acc = 0.6962
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 95, n_samples = 269/862, baseline = 0.5278, acc = 0.6965
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 96, n_samples = 251/862, baseline = 0.5565, acc = 0.6972
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 97, n_samples = 254/862, baseline = 0.5362, acc = 0.6875
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 98, n_samples = 246/862, baseline = 0.5341, acc = 0.6802
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 99, n_samples = 250/862, baseline = 0.5507, acc = 0.6765
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 0, n_samples = 244/862, baseline = 0.5502, acc = 0.6845
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 1, n_samples = 277/862, baseline = 0.5368, acc = 0.7026
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 2, n_samples = 264/862, baseline = 0.5552, acc = 0.7074
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 3, n_samples = 250/862, baseline = 0.5441, acc = 0.7010
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 4, n_samples = 231/862, baseline = 0.5452, acc = 0.6910
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 5, n_samples = 248/862, baseline = 0.5521, acc = 0.6840
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 6, n_samples = 243/862, baseline = 0.5412, acc = 0.6947
-- [u'Sex', 'Age', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 7, n_samples = 266/862, baseline = 0.5419, acc = 0.6678
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 8, n_samples = 278/862, baseline = 0.5599, acc = 0.6918
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 9, n_samples = 276/862, baseline = 0.5563, acc = 0.6792
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 10, n_samples = 265/862, baseline = 0.5511, acc = 0.7018
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 11, n_samples = 285/862, baseline = 0.5546, acc = 0.6932
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 12, n_samples = 262/862, baseline = 0.5517, acc = 0.7117
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 13, n_samples = 260/862, baseline = 0.5365, acc = 0.6827
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 14, n_samples = 231/862, baseline = 0.5468, acc = 0.6957
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 15, n_samples = 234/862, baseline = 0.5494, acc = 0.6831
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 16, n_samples = 252/862, baseline = 0.5492, acc = 0.6836
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 17, n_samples = 249/862, baseline = 0.5383, acc = 0.6819
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 18, n_samples = 258/862, baseline = 0.5480, acc = 0.6788
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 19, n_samples = 254/862, baseline = 0.5559, acc = 0.6974
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 20, n_samples = 285/862, baseline = 0.5633, acc = 0.6915
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 21, n_samples = 243/862, baseline = 0.5493, acc = 0.6769
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 22, n_samples = 285/862, baseline = 0.5581, acc = 0.6828
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 23, n_samples = 277/862, baseline = 0.5556, acc = 0.6957
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 24, n_samples = 258/862, baseline = 0.5662, acc = 0.6738
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 25, n_samples = 261/862, baseline = 0.5341, acc = 0.6905
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 26, n_samples = 258/862, baseline = 0.5613, acc = 0.6887
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 27, n_samples = 255/862, baseline = 0.5601, acc = 0.6771
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 28, n_samples = 246/862, baseline = 0.5390, acc = 0.7029
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 29, n_samples = 253/862, baseline = 0.5517, acc = 0.6946
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 30, n_samples = 256/862, baseline = 0.5594, acc = 0.7162
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 31, n_samples = 243/862, baseline = 0.5347, acc = 0.6995
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 32, n_samples = 263/862, baseline = 0.5609, acc = 0.7145
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 33, n_samples = 260/862, baseline = 0.5233, acc = 0.6595
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 34, n_samples = 249/862, baseline = 0.5644, acc = 0.6803
-- ['Age', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 35, n_samples = 234/862, baseline = 0.5541, acc = 0.7054
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 36, n_samples = 295/862, baseline = 0.5467, acc = 0.6931
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 37, n_samples = 280/862, baseline = 0.5533, acc = 0.6890
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 38, n_samples = 273/862, baseline = 0.5603, acc = 0.6910
-- ['Age', u'SM', u'Max D', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 39, n_samples = 260/862, baseline = 0.5415, acc = 0.6794
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 40, n_samples = 278/862, baseline = 0.5462, acc = 0.6952
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 41, n_samples = 244/862, baseline = 0.5421, acc = 0.7087
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 42, n_samples = 252/862, baseline = 0.5410, acc = 0.6918
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 43, n_samples = 285/862, baseline = 0.5269, acc = 0.6603
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 44, n_samples = 263/862, baseline = 0.5609, acc = 0.6745
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 45, n_samples = 246/862, baseline = 0.5487, acc = 0.7127
-- [u'Sex', 'Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 46, n_samples = 248/862, baseline = 0.5521, acc = 0.6922
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 47, n_samples = 269/862, baseline = 0.5430, acc = 0.6830
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 48, n_samples = 278/862, baseline = 0.5531, acc = 0.6935
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 49, n_samples = 259/862, baseline = 0.5605, acc = 0.7065
-- [u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 50, n_samples = 278/862, baseline = 0.5531, acc = 0.6918
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 51, n_samples = 263/862, baseline = 0.5409, acc = 0.6761
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 52, n_samples = 261/862, baseline = 0.5740, acc = 0.7088
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 53, n_samples = 233/862, baseline = 0.5692, acc = 0.6963
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 54, n_samples = 247/862, baseline = 0.5512, acc = 0.6894
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 55, n_samples = 238/862, baseline = 0.5433, acc = 0.6907
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 56, n_samples = 266/862, baseline = 0.5587, acc = 0.6728
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 57, n_samples = 279/862, baseline = 0.5626, acc = 0.7136
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 58, n_samples = 249/862, baseline = 0.5498, acc = 0.6949
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 59, n_samples = 240/862, baseline = 0.5450, acc = 0.6768
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 60, n_samples = 251/862, baseline = 0.5532, acc = 0.7054
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 61, n_samples = 256/862, baseline = 0.5347, acc = 0.6848
-- [u'Sex', 'Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 62, n_samples = 285/862, baseline = 0.5234, acc = 0.6932
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 63, n_samples = 261/862, baseline = 0.5491, acc = 0.6938
-- [u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 64, n_samples = 238/862, baseline = 0.5465, acc = 0.6875
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 65, n_samples = 254/862, baseline = 0.5691, acc = 0.7286
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 66, n_samples = 260/862, baseline = 0.5482, acc = 0.6877
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 67, n_samples = 252/862, baseline = 0.5443, acc = 0.7131
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 68, n_samples = 273/862, baseline = 0.5569, acc = 0.6978
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 69, n_samples = 245/862, baseline = 0.5656, acc = 0.6904
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 70, n_samples = 265/862, baseline = 0.5511, acc = 0.6834
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 71, n_samples = 263/862, baseline = 0.5442, acc = 0.6628
-- [u'Sex', 'Age', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 72, n_samples = 230/862, baseline = 0.5601, acc = 0.6978
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 73, n_samples = 280/862, baseline = 0.5601, acc = 0.7045
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 74, n_samples = 264/862, baseline = 0.5502, acc = 0.7057
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 75, n_samples = 256/862, baseline = 0.5561, acc = 0.7063
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 76, n_samples = 257/862, baseline = 0.5405, acc = 0.6744
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 77, n_samples = 266/862, baseline = 0.5436, acc = 0.6661
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 78, n_samples = 256/862, baseline = 0.5479, acc = 0.6914
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 79, n_samples = 242/862, baseline = 0.5484, acc = 0.7016
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 80, n_samples = 270/862, baseline = 0.5405, acc = 0.6774
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 81, n_samples = 276/862, baseline = 0.5375, acc = 0.6860
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 82, n_samples = 267/862, baseline = 0.5496, acc = 0.6807
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 83, n_samples = 254/862, baseline = 0.5477, acc = 0.6941
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 84, n_samples = 262/862, baseline = 0.5367, acc = 0.7017
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 85, n_samples = 284/862, baseline = 0.5675, acc = 0.6955
-- [u'Sex', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 86, n_samples = 261/862, baseline = 0.5524, acc = 0.6988
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 87, n_samples = 283/862, baseline = 0.5475, acc = 0.6960
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 88, n_samples = 251/862, baseline = 0.5499, acc = 0.6809
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 89, n_samples = 232/862, baseline = 0.5413, acc = 0.6762
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 90, n_samples = 255/862, baseline = 0.5437, acc = 0.7018
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 91, n_samples = 260/862, baseline = 0.5698, acc = 0.6794
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 92, n_samples = 285/862, baseline = 0.5459, acc = 0.7019
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 93, n_samples = 261/862, baseline = 0.5458, acc = 0.6905
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 94, n_samples = 251/862, baseline = 0.5237, acc = 0.6661
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 95, n_samples = 266/862, baseline = 0.5453, acc = 0.7097
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 96, n_samples = 268/862, baseline = 0.5606, acc = 0.6852
-- [u'Sex', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 97, n_samples = 247/862, baseline = 0.5512, acc = 0.7024
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 98, n_samples = 270/862, baseline = 0.5490, acc = 0.6706
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 99, n_samples = 252/862, baseline = 0.5459, acc = 0.6770
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 0, n_samples = 169/862, baseline = 0.5426, acc = 0.6999
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 1, n_samples = 166/862, baseline = 0.5560, acc = 0.6652
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 2, n_samples = 165/862, baseline = 0.5524, acc = 0.6901
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 3, n_samples = 153/862, baseline = 0.5529, acc = 0.6615
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 4, n_samples = 177/862, baseline = 0.5489, acc = 0.6788
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 5, n_samples = 188/862, baseline = 0.5371, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 6, n_samples = 184/862, baseline = 0.5457, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 7, n_samples = 163/862, baseline = 0.5622, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 8, n_samples = 158/862, baseline = 0.5554, acc = 0.6918
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 9, n_samples = 188/862, baseline = 0.5475, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 10, n_samples = 183/862, baseline = 0.5552, acc = 0.6642
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 11, n_samples = 176/862, baseline = 0.5350, acc = 0.6618
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 12, n_samples = 180/862, baseline = 0.5601, acc = 0.6994
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 13, n_samples = 168/862, baseline = 0.5519, acc = 0.6729
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 14, n_samples = 162/862, baseline = 0.5657, acc = 0.6700
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 15, n_samples = 167/862, baseline = 0.5525, acc = 0.6863
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 16, n_samples = 194/862, baseline = 0.5494, acc = 0.6856
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 17, n_samples = 180/862, baseline = 0.5425, acc = 0.6701
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 18, n_samples = 165/862, baseline = 0.5352, acc = 0.6600
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 19, n_samples = 207/862, baseline = 0.5511, acc = 0.6595
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 20, n_samples = 178/862, baseline = 0.5512, acc = 0.6901
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 21, n_samples = 175/862, baseline = 0.5619, acc = 0.6769
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 22, n_samples = 162/862, baseline = 0.5643, acc = 0.6686
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 23, n_samples = 176/862, baseline = 0.5394, acc = 0.6531
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 24, n_samples = 174/862, baseline = 0.5494, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 25, n_samples = 162/862, baseline = 0.5500, acc = 0.6471
-- [u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 26, n_samples = 175/862, baseline = 0.5648, acc = 0.6725
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 27, n_samples = 182/862, baseline = 0.5603, acc = 0.6750
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 28, n_samples = 163/862, baseline = 0.5508, acc = 0.6795
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 29, n_samples = 173/862, baseline = 0.5414, acc = 0.6792
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 30, n_samples = 160/862, baseline = 0.5413, acc = 0.6738
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 31, n_samples = 173/862, baseline = 0.5530, acc = 0.6967
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 32, n_samples = 137/862, baseline = 0.5517, acc = 0.6786
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 33, n_samples = 177/862, baseline = 0.5474, acc = 0.6759
-- [u'Max D', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 34, n_samples = 166/862, baseline = 0.5489, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 35, n_samples = 178/862, baseline = 0.5497, acc = 0.6959
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 36, n_samples = 199/862, baseline = 0.5641, acc = 0.6908
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 37, n_samples = 159/862, baseline = 0.5562, acc = 0.6885
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 38, n_samples = 163/862, baseline = 0.5408, acc = 0.6838
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 39, n_samples = 191/862, baseline = 0.5469, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 40, n_samples = 212/862, baseline = 0.5646, acc = 0.6754
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 41, n_samples = 181/862, baseline = 0.5463, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 42, n_samples = 185/862, baseline = 0.5495, acc = 0.6883
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 43, n_samples = 177/862, baseline = 0.5416, acc = 0.6599
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 44, n_samples = 173/862, baseline = 0.5486, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 45, n_samples = 200/862, baseline = 0.5468, acc = 0.6949
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 46, n_samples = 159/862, baseline = 0.5519, acc = 0.6757
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 47, n_samples = 182/862, baseline = 0.5382, acc = 0.6809
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 48, n_samples = 172/862, baseline = 0.5522, acc = 0.6826
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 49, n_samples = 159/862, baseline = 0.5576, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 50, n_samples = 166/862, baseline = 0.5474, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 51, n_samples = 170/862, baseline = 0.5462, acc = 0.6749
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 52, n_samples = 182/862, baseline = 0.5485, acc = 0.6971
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 53, n_samples = 157/862, baseline = 0.5617, acc = 0.6440
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 54, n_samples = 194/862, baseline = 0.5434, acc = 0.6751
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 55, n_samples = 181/862, baseline = 0.5448, acc = 0.6828
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 56, n_samples = 187/862, baseline = 0.5600, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 57, n_samples = 171/862, baseline = 0.5615, acc = 0.6758
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 58, n_samples = 181/862, baseline = 0.5463, acc = 0.6843
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 59, n_samples = 179/862, baseline = 0.5578, acc = 0.6779
-- [u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 60, n_samples = 176/862, baseline = 0.5335, acc = 0.6531
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 61, n_samples = 143/862, baseline = 0.5605, acc = 0.6565
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 62, n_samples = 176/862, baseline = 0.5408, acc = 0.6706
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 63, n_samples = 161/862, baseline = 0.5563, acc = 0.6947
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 64, n_samples = 171/862, baseline = 0.5543, acc = 0.6585
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 65, n_samples = 160/862, baseline = 0.5456, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 66, n_samples = 174/862, baseline = 0.5465, acc = 0.6512
-- ['Age', u'Max D', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 67, n_samples = 176/862, baseline = 0.5612, acc = 0.6749
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 68, n_samples = 164/862, baseline = 0.5559, acc = 0.6777
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 69, n_samples = 183/862, baseline = 0.5479, acc = 0.6730
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 70, n_samples = 153/862, baseline = 0.5656, acc = 0.6770
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 71, n_samples = 178/862, baseline = 0.5526, acc = 0.6944
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 72, n_samples = 184/862, baseline = 0.5516, acc = 0.6652
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 73, n_samples = 170/862, baseline = 0.5535, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 74, n_samples = 164/862, baseline = 0.5774, acc = 0.6547
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 75, n_samples = 154/862, baseline = 0.5636, acc = 0.6723
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 76, n_samples = 167/862, baseline = 0.5367, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 77, n_samples = 177/862, baseline = 0.5723, acc = 0.6803
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 78, n_samples = 179/862, baseline = 0.5388, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 79, n_samples = 170/862, baseline = 0.5549, acc = 0.6734
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 80, n_samples = 184/862, baseline = 0.5457, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 81, n_samples = 164/862, baseline = 0.5501, acc = 0.6905
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 82, n_samples = 177/862, baseline = 0.5460, acc = 0.6891
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 83, n_samples = 168/862, baseline = 0.5519, acc = 0.6556
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 84, n_samples = 209/862, baseline = 0.5513, acc = 0.6723
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 85, n_samples = 183/862, baseline = 0.5420, acc = 0.6922
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 86, n_samples = 195/862, baseline = 0.5472, acc = 0.6807
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 87, n_samples = 161/862, baseline = 0.5506, acc = 0.6904
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 88, n_samples = 180/862, baseline = 0.5601, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 89, n_samples = 155/862, baseline = 0.5530, acc = 0.6775
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 90, n_samples = 160/862, baseline = 0.5684, acc = 0.6695
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=False iter = 91, n_samples = 176/862, baseline = 0.5525, acc = 0.6822
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 92, n_samples = 175/862, baseline = 0.5415, acc = 0.6812
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 93, n_samples = 158/862, baseline = 0.5568, acc = 0.6974
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 94, n_samples = 172/862, baseline = 0.5565, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 95, n_samples = 180/862, baseline = 0.5396, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 96, n_samples = 159/862, baseline = 0.5519, acc = 0.6856
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 97, n_samples = 155/862, baseline = 0.5446, acc = 0.6478
-- ['Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=False iter = 98, n_samples = 179/862, baseline = 0.5622, acc = 0.6808
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=False iter = 99, n_samples = 145/862, baseline = 0.5523, acc = 0.6681
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 0, n_samples = 161/862, baseline = 0.5421, acc = 0.6705
-- [u'Sex', 'Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 1, n_samples = 181/862, baseline = 0.5565, acc = 0.6784
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 2, n_samples = 175/862, baseline = 0.5488, acc = 0.7031
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 3, n_samples = 182/862, baseline = 0.5485, acc = 0.6956
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 4, n_samples = 179/862, baseline = 0.5520, acc = 0.7057
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 5, n_samples = 151/862, baseline = 0.5457, acc = 0.6793
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 6, n_samples = 178/862, baseline = 0.5658, acc = 0.6988
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 7, n_samples = 179/862, baseline = 0.5461, acc = 0.6911
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 8, n_samples = 167/862, baseline = 0.5525, acc = 0.7022
-- [u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 9, n_samples = 175/862, baseline = 0.5269, acc = 0.6739
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 10, n_samples = 154/862, baseline = 0.5565, acc = 0.6921
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 11, n_samples = 151/862, baseline = 0.5570, acc = 0.6850
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 12, n_samples = 158/862, baseline = 0.5540, acc = 0.6861
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 13, n_samples = 170/862, baseline = 0.5549, acc = 0.6517
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 14, n_samples = 171/862, baseline = 0.5630, acc = 0.6990
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 15, n_samples = 159/862, baseline = 0.5420, acc = 0.6913
-- [u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 16, n_samples = 182/862, baseline = 0.5618, acc = 0.6882
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 17, n_samples = 185/862, baseline = 0.5702, acc = 0.6942
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 18, n_samples = 165/862, baseline = 0.5610, acc = 0.6944
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 19, n_samples = 155/862, baseline = 0.5502, acc = 0.6747
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 20, n_samples = 145/862, baseline = 0.5384, acc = 0.6709
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 21, n_samples = 142/862, baseline = 0.5514, acc = 0.6931
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 22, n_samples = 169/862, baseline = 0.5671, acc = 0.6710
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 23, n_samples = 183/862, baseline = 0.5655, acc = 0.6775
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 24, n_samples = 184/862, baseline = 0.5560, acc = 0.6785
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 25, n_samples = 168/862, baseline = 0.5476, acc = 0.6873
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 26, n_samples = 180/862, baseline = 0.5689, acc = 0.6496
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 27, n_samples = 180/862, baseline = 0.5411, acc = 0.6584
-- ['Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 28, n_samples = 174/862, baseline = 0.5509, acc = 0.6817
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 29, n_samples = 187/862, baseline = 0.5378, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 30, n_samples = 182/862, baseline = 0.5426, acc = 0.6794
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 31, n_samples = 162/862, baseline = 0.5471, acc = 0.6800
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 32, n_samples = 180/862, baseline = 0.5352, acc = 0.6598
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 33, n_samples = 170/862, baseline = 0.5607, acc = 0.6691
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 34, n_samples = 187/862, baseline = 0.5615, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 35, n_samples = 174/862, baseline = 0.5436, acc = 0.6831
-- ['Age', u'SM', u'Max D', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 36, n_samples = 162/862, baseline = 0.5486, acc = 0.6800
-- [u'Sex', 'Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 37, n_samples = 184/862, baseline = 0.5354, acc = 0.6829
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 38, n_samples = 164/862, baseline = 0.5559, acc = 0.7034
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 39, n_samples = 185/862, baseline = 0.5583, acc = 0.6795
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 40, n_samples = 170/862, baseline = 0.5448, acc = 0.6647
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 41, n_samples = 176/862, baseline = 0.5525, acc = 0.6910
-- ['Age', u'Max D', 'Volume', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 42, n_samples = 171/862, baseline = 0.5572, acc = 0.7120
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 43, n_samples = 186/862, baseline = 0.5473, acc = 0.6701
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 44, n_samples = 189/862, baseline = 0.5691, acc = 0.6493
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 45, n_samples = 169/862, baseline = 0.5556, acc = 0.6753
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 46, n_samples = 158/862, baseline = 0.5213, acc = 0.6520
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 47, n_samples = 171/862, baseline = 0.5644, acc = 0.6903
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 48, n_samples = 173/862, baseline = 0.5544, acc = 0.6560
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 49, n_samples = 179/862, baseline = 0.5417, acc = 0.6779
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 50, n_samples = 157/862, baseline = 0.5504, acc = 0.6894
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 51, n_samples = 159/862, baseline = 0.5519, acc = 0.6885
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 52, n_samples = 161/862, baseline = 0.5321, acc = 0.6662
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 53, n_samples = 163/862, baseline = 0.5451, acc = 0.6896
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 54, n_samples = 170/862, baseline = 0.5535, acc = 0.6720
-- ['Age', u'SM', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 55, n_samples = 157/862, baseline = 0.5518, acc = 0.6610
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 56, n_samples = 187/862, baseline = 0.5467, acc = 0.6785
-- ['Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 57, n_samples = 158/862, baseline = 0.5554, acc = 0.6364
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 58, n_samples = 170/862, baseline = 0.5564, acc = 0.6749
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 59, n_samples = 170/862, baseline = 0.5477, acc = 0.6662
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 60, n_samples = 159/862, baseline = 0.5462, acc = 0.6629
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 61, n_samples = 179/862, baseline = 0.5461, acc = 0.6589
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 62, n_samples = 159/862, baseline = 0.5576, acc = 0.6600
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 63, n_samples = 156/862, baseline = 0.5510, acc = 0.7011
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 64, n_samples = 175/862, baseline = 0.5502, acc = 0.6841
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 65, n_samples = 172/862, baseline = 0.5478, acc = 0.6609
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 66, n_samples = 174/862, baseline = 0.5422, acc = 0.6759
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 67, n_samples = 177/862, baseline = 0.5533, acc = 0.6876
-- [u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 68, n_samples = 169/862, baseline = 0.5556, acc = 0.6955
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 69, n_samples = 172/862, baseline = 0.5493, acc = 0.6870
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 70, n_samples = 195/862, baseline = 0.5517, acc = 0.7136
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 71, n_samples = 170/862, baseline = 0.5549, acc = 0.6908
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 72, n_samples = 177/862, baseline = 0.5504, acc = 0.6715
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 73, n_samples = 175/862, baseline = 0.5517, acc = 0.6798
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 74, n_samples = 159/862, baseline = 0.5477, acc = 0.6785
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 75, n_samples = 182/862, baseline = 0.5529, acc = 0.6941
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=False iter = 76, n_samples = 177/862, baseline = 0.5533, acc = 0.6993
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 77, n_samples = 168/862, baseline = 0.5447, acc = 0.6859
-- ['Age', u'Max D', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 78, n_samples = 153/862, baseline = 0.5515, acc = 0.6812
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 79, n_samples = 155/862, baseline = 0.5516, acc = 0.6860
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 80, n_samples = 185/862, baseline = 0.5495, acc = 0.6957
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 81, n_samples = 191/862, baseline = 0.5633, acc = 0.7049
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 82, n_samples = 167/862, baseline = 0.5511, acc = 0.6921
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 83, n_samples = 161/862, baseline = 0.5492, acc = 0.6519
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 84, n_samples = 166/862, baseline = 0.5374, acc = 0.6624
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 85, n_samples = 186/862, baseline = 0.5399, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 86, n_samples = 174/862, baseline = 0.5596, acc = 0.6919
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 87, n_samples = 174/862, baseline = 0.5523, acc = 0.6642
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 88, n_samples = 178/862, baseline = 0.5585, acc = 0.6725
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 89, n_samples = 167/862, baseline = 0.5367, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 90, n_samples = 168/862, baseline = 0.5605, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 91, n_samples = 156/862, baseline = 0.5552, acc = 0.6827
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 92, n_samples = 178/862, baseline = 0.5629, acc = 0.6871
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 93, n_samples = 163/862, baseline = 0.5393, acc = 0.6824
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 94, n_samples = 164/862, baseline = 0.5501, acc = 0.6877
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 95, n_samples = 170/862, baseline = 0.5477, acc = 0.6821
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=False iter = 96, n_samples = 166/862, baseline = 0.5417, acc = 0.6853
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 97, n_samples = 162/862, baseline = 0.5457, acc = 0.6486
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=False iter = 98, n_samples = 169/862, baseline = 0.5455, acc = 0.6522
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=False iter = 99, n_samples = 147/862, baseline = 0.5441, acc = 0.7049
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 0, n_samples = 180/862, baseline = 0.5469, acc = 0.6994
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 1, n_samples = 180/862, baseline = 0.5484, acc = 0.7009
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 2, n_samples = 172/862, baseline = 0.5493, acc = 0.6768
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 3, n_samples = 165/862, baseline = 0.5395, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 4, n_samples = 176/862, baseline = 0.5466, acc = 0.6545
-- [u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 5, n_samples = 169/862, baseline = 0.5483, acc = 0.6724
-- ['Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 6, n_samples = 189/862, baseline = 0.5498, acc = 0.6686
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 7, n_samples = 159/862, baseline = 0.5448, acc = 0.6885
-- ['Age', u'Max D', 'Volume', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 8, n_samples = 186/862, baseline = 0.5414, acc = 0.6879
-- ['Age', u'Max D', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 9, n_samples = 181/862, baseline = 0.5653, acc = 0.6579
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 10, n_samples = 165/862, baseline = 0.5595, acc = 0.6915
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 11, n_samples = 178/862, baseline = 0.5556, acc = 0.6988
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 12, n_samples = 183/862, baseline = 0.5449, acc = 0.6966
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 13, n_samples = 170/862, baseline = 0.5592, acc = 0.6777
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 14, n_samples = 174/862, baseline = 0.5480, acc = 0.6817
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 15, n_samples = 157/862, baseline = 0.5447, acc = 0.6766
-- ['Age', u'SM', u'Max D', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 16, n_samples = 180/862, baseline = 0.5367, acc = 0.6789
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 17, n_samples = 159/862, baseline = 0.5448, acc = 0.6615
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 18, n_samples = 175/862, baseline = 0.5444, acc = 0.6987
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 19, n_samples = 166/862, baseline = 0.5532, acc = 0.6710
-- [u'Sex', 'Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 20, n_samples = 179/862, baseline = 0.5359, acc = 0.6867
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 21, n_samples = 176/862, baseline = 0.5729, acc = 0.6866
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 22, n_samples = 146/862, baseline = 0.5517, acc = 0.6788
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 23, n_samples = 184/862, baseline = 0.5457, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 24, n_samples = 188/862, baseline = 0.5593, acc = 0.6795
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 25, n_samples = 174/862, baseline = 0.5494, acc = 0.6802
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 26, n_samples = 180/862, baseline = 0.5616, acc = 0.6701
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 27, n_samples = 174/862, baseline = 0.5436, acc = 0.6584
-- [u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 28, n_samples = 171/862, baseline = 0.5543, acc = 0.6700
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 29, n_samples = 172/862, baseline = 0.5420, acc = 0.6797
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 30, n_samples = 156/862, baseline = 0.5482, acc = 0.7068
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 31, n_samples = 175/862, baseline = 0.5546, acc = 0.6754
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 32, n_samples = 171/862, baseline = 0.5384, acc = 0.6874
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 33, n_samples = 164/862, baseline = 0.5587, acc = 0.6791
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 34, n_samples = 150/862, baseline = 0.5702, acc = 0.6643
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 35, n_samples = 173/862, baseline = 0.5660, acc = 0.6909
-- [u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 36, n_samples = 170/862, baseline = 0.5506, acc = 0.6662
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 37, n_samples = 162/862, baseline = 0.5643, acc = 0.6929
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 38, n_samples = 172/862, baseline = 0.5623, acc = 0.7043
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 39, n_samples = 170/862, baseline = 0.5462, acc = 0.6893
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 40, n_samples = 175/862, baseline = 0.5400, acc = 0.6943
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 41, n_samples = 177/862, baseline = 0.5577, acc = 0.6482
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 42, n_samples = 187/862, baseline = 0.5511, acc = 0.6919
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 43, n_samples = 162/862, baseline = 0.5471, acc = 0.7000
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 44, n_samples = 177/862, baseline = 0.5504, acc = 0.6920
-- [u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 45, n_samples = 180/862, baseline = 0.5543, acc = 0.6833
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 46, n_samples = 191/862, baseline = 0.5469, acc = 0.6870
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 47, n_samples = 170/862, baseline = 0.5506, acc = 0.6850
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 48, n_samples = 181/862, baseline = 0.5448, acc = 0.6858
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 49, n_samples = 181/862, baseline = 0.5507, acc = 0.6843
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 50, n_samples = 174/862, baseline = 0.5465, acc = 0.6933
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 51, n_samples = 165/862, baseline = 0.5438, acc = 0.6743
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 52, n_samples = 179/862, baseline = 0.5490, acc = 0.7013
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 53, n_samples = 167/862, baseline = 0.5482, acc = 0.6748
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 54, n_samples = 166/862, baseline = 0.5460, acc = 0.6925
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 55, n_samples = 176/862, baseline = 0.5408, acc = 0.6691
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 56, n_samples = 155/862, baseline = 0.5559, acc = 0.6535
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 57, n_samples = 172/862, baseline = 0.5507, acc = 0.6754
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 58, n_samples = 155/862, baseline = 0.5545, acc = 0.6902
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 59, n_samples = 171/862, baseline = 0.5441, acc = 0.6831
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 60, n_samples = 170/862, baseline = 0.5520, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 61, n_samples = 156/862, baseline = 0.5453, acc = 0.6884
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 62, n_samples = 163/862, baseline = 0.5494, acc = 0.6910
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 63, n_samples = 169/862, baseline = 0.5599, acc = 0.6869
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 64, n_samples = 172/862, baseline = 0.5406, acc = 0.6725
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 65, n_samples = 161/862, baseline = 0.5578, acc = 0.6748
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 66, n_samples = 185/862, baseline = 0.5480, acc = 0.6869
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 67, n_samples = 156/862, baseline = 0.5567, acc = 0.6572
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 68, n_samples = 174/862, baseline = 0.5654, acc = 0.6715
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 69, n_samples = 161/862, baseline = 0.5521, acc = 0.6961
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 70, n_samples = 173/862, baseline = 0.5443, acc = 0.6763
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 71, n_samples = 176/862, baseline = 0.5539, acc = 0.6895
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 72, n_samples = 175/862, baseline = 0.5590, acc = 0.6827
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 73, n_samples = 200/862, baseline = 0.5529, acc = 0.7145
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 74, n_samples = 171/862, baseline = 0.5601, acc = 0.7004
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 75, n_samples = 177/862, baseline = 0.5504, acc = 0.6730
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 76, n_samples = 151/862, baseline = 0.5513, acc = 0.6906
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 77, n_samples = 172/862, baseline = 0.5435, acc = 0.6913
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 78, n_samples = 172/862, baseline = 0.5464, acc = 0.6449
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 79, n_samples = 175/862, baseline = 0.5415, acc = 0.6885
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 80, n_samples = 167/862, baseline = 0.5482, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 81, n_samples = 164/862, baseline = 0.5458, acc = 0.6819
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=False iter = 82, n_samples = 149/862, baseline = 0.5470, acc = 0.6999
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 83, n_samples = 172/862, baseline = 0.5507, acc = 0.6855
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 84, n_samples = 176/862, baseline = 0.5350, acc = 0.6793
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 85, n_samples = 189/862, baseline = 0.5557, acc = 0.6538
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 86, n_samples = 171/862, baseline = 0.5427, acc = 0.7004
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 87, n_samples = 171/862, baseline = 0.5470, acc = 0.6874
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 88, n_samples = 174/862, baseline = 0.5596, acc = 0.6904
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 89, n_samples = 163/862, baseline = 0.5479, acc = 0.6953
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 90, n_samples = 160/862, baseline = 0.5442, acc = 0.6695
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 91, n_samples = 172/862, baseline = 0.5551, acc = 0.6928
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 92, n_samples = 184/862, baseline = 0.5487, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 93, n_samples = 179/862, baseline = 0.5564, acc = 0.6955
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 94, n_samples = 161/862, baseline = 0.5535, acc = 0.6776
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 95, n_samples = 167/862, baseline = 0.5496, acc = 0.6676
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 96, n_samples = 173/862, baseline = 0.5515, acc = 0.6952
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 97, n_samples = 182/862, baseline = 0.5456, acc = 0.6897
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=False iter = 98, n_samples = 186/862, baseline = 0.5695, acc = 0.6583
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=False iter = 99, n_samples = 182/862, baseline = 0.5618, acc = 0.6868
-- [u'Sex', 'Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0200, intercept=True iter = 0, n_samples = 175/862, baseline = 0.5502, acc = 0.6739
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 1, n_samples = 173/862, baseline = 0.5559, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 2, n_samples = 169/862, baseline = 0.5469, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 3, n_samples = 184/862, baseline = 0.5413, acc = 0.6740
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 4, n_samples = 185/862, baseline = 0.5554, acc = 0.6809
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 5, n_samples = 180/862, baseline = 0.5411, acc = 0.6833
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 6, n_samples = 182/862, baseline = 0.5559, acc = 0.6868
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 7, n_samples = 187/862, baseline = 0.5481, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 8, n_samples = 181/862, baseline = 0.5507, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 9, n_samples = 177/862, baseline = 0.5547, acc = 0.6788
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 10, n_samples = 165/862, baseline = 0.5552, acc = 0.6801
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 11, n_samples = 170/862, baseline = 0.5419, acc = 0.6850
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 12, n_samples = 184/862, baseline = 0.5619, acc = 0.6903
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 13, n_samples = 161/862, baseline = 0.5464, acc = 0.6819
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 14, n_samples = 196/862, baseline = 0.5556, acc = 0.6532
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 15, n_samples = 171/862, baseline = 0.5586, acc = 0.6599
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 16, n_samples = 188/862, baseline = 0.5697, acc = 0.6944
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 17, n_samples = 183/862, baseline = 0.5567, acc = 0.6701
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 18, n_samples = 172/862, baseline = 0.5362, acc = 0.6696
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 19, n_samples = 195/862, baseline = 0.5442, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 20, n_samples = 166/862, baseline = 0.5560, acc = 0.6753
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 21, n_samples = 162/862, baseline = 0.5514, acc = 0.6800
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 22, n_samples = 185/862, baseline = 0.5318, acc = 0.6544
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 23, n_samples = 191/862, baseline = 0.5499, acc = 0.6811
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 24, n_samples = 177/862, baseline = 0.5518, acc = 0.6920
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 25, n_samples = 168/862, baseline = 0.5403, acc = 0.6729
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 26, n_samples = 183/862, baseline = 0.5552, acc = 0.6863
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 27, n_samples = 167/862, baseline = 0.5381, acc = 0.6590
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 28, n_samples = 188/862, baseline = 0.5415, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 29, n_samples = 182/862, baseline = 0.5485, acc = 0.6926
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 30, n_samples = 167/862, baseline = 0.5597, acc = 0.6748
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 31, n_samples = 157/862, baseline = 0.5546, acc = 0.6709
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 32, n_samples = 174/862, baseline = 0.5552, acc = 0.6788
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 33, n_samples = 176/862, baseline = 0.5466, acc = 0.6808
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 34, n_samples = 176/862, baseline = 0.5437, acc = 0.6662
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 35, n_samples = 179/862, baseline = 0.5461, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 36, n_samples = 166/862, baseline = 0.5575, acc = 0.6839
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 37, n_samples = 186/862, baseline = 0.5577, acc = 0.7012
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 38, n_samples = 161/862, baseline = 0.5464, acc = 0.6933
-- [u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 39, n_samples = 184/862, baseline = 0.5428, acc = 0.6711
-- ['Age', u'Max D', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 40, n_samples = 171/862, baseline = 0.5586, acc = 0.6961
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 41, n_samples = 182/862, baseline = 0.5324, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 42, n_samples = 152/862, baseline = 0.5394, acc = 0.6704
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 43, n_samples = 166/862, baseline = 0.5603, acc = 0.6954
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 44, n_samples = 173/862, baseline = 0.5385, acc = 0.6763
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 45, n_samples = 179/862, baseline = 0.5637, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 46, n_samples = 173/862, baseline = 0.5501, acc = 0.6894
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 47, n_samples = 180/862, baseline = 0.5484, acc = 0.6760
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 48, n_samples = 160/862, baseline = 0.5313, acc = 0.6624
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 49, n_samples = 161/862, baseline = 0.5478, acc = 0.6862
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 50, n_samples = 200/862, baseline = 0.5514, acc = 0.6903
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 51, n_samples = 148/862, baseline = 0.5490, acc = 0.6835
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 52, n_samples = 177/862, baseline = 0.5518, acc = 0.6920
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 53, n_samples = 168/862, baseline = 0.5447, acc = 0.6758
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 54, n_samples = 189/862, baseline = 0.5483, acc = 0.6746
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 55, n_samples = 170/862, baseline = 0.5535, acc = 0.6749
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 56, n_samples = 161/862, baseline = 0.5506, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 57, n_samples = 191/862, baseline = 0.5499, acc = 0.6736
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 58, n_samples = 199/862, baseline = 0.5475, acc = 0.6757
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 59, n_samples = 179/862, baseline = 0.5432, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 60, n_samples = 202/862, baseline = 0.5652, acc = 0.6788
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 61, n_samples = 185/862, baseline = 0.5554, acc = 0.6632
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 62, n_samples = 168/862, baseline = 0.5576, acc = 0.6772
-- ['Age', u'Max D', 'Volume', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 63, n_samples = 162/862, baseline = 0.5486, acc = 0.6829
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 64, n_samples = 165/862, baseline = 0.5509, acc = 0.6729
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 65, n_samples = 182/862, baseline = 0.5382, acc = 0.6706
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 66, n_samples = 162/862, baseline = 0.5500, acc = 0.6900
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 67, n_samples = 201/862, baseline = 0.5567, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 68, n_samples = 166/862, baseline = 0.5431, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 69, n_samples = 172/862, baseline = 0.5522, acc = 0.6899
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 70, n_samples = 167/862, baseline = 0.5554, acc = 0.6892
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 71, n_samples = 176/862, baseline = 0.5671, acc = 0.6735
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 72, n_samples = 180/862, baseline = 0.5396, acc = 0.6613
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 73, n_samples = 175/862, baseline = 0.5560, acc = 0.6929
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 74, n_samples = 176/862, baseline = 0.5525, acc = 0.6764
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 75, n_samples = 157/862, baseline = 0.5461, acc = 0.6865
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 76, n_samples = 168/862, baseline = 0.5476, acc = 0.6571
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 77, n_samples = 160/862, baseline = 0.5584, acc = 0.6880
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 78, n_samples = 155/862, baseline = 0.5601, acc = 0.6492
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 79, n_samples = 158/862, baseline = 0.5497, acc = 0.6719
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 80, n_samples = 178/862, baseline = 0.5409, acc = 0.6769
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 81, n_samples = 162/862, baseline = 0.5500, acc = 0.6800
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 82, n_samples = 176/862, baseline = 0.5481, acc = 0.6778
-- ['Age', u'Max D', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 83, n_samples = 172/862, baseline = 0.5478, acc = 0.6652
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 84, n_samples = 171/862, baseline = 0.5557, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 85, n_samples = 191/862, baseline = 0.5618, acc = 0.6811
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 86, n_samples = 164/862, baseline = 0.5458, acc = 0.6619
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 87, n_samples = 165/862, baseline = 0.5423, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 88, n_samples = 187/862, baseline = 0.5585, acc = 0.6874
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 89, n_samples = 168/862, baseline = 0.5418, acc = 0.6715
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 90, n_samples = 171/862, baseline = 0.5456, acc = 0.6773
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 91, n_samples = 186/862, baseline = 0.5459, acc = 0.6864
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 92, n_samples = 162/862, baseline = 0.5600, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 93, n_samples = 177/862, baseline = 0.5474, acc = 0.6832
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 94, n_samples = 169/862, baseline = 0.5440, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 95, n_samples = 175/862, baseline = 0.5444, acc = 0.6798
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0200, intercept=True iter = 96, n_samples = 178/862, baseline = 0.5614, acc = 0.7120
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0200, intercept=True iter = 97, n_samples = 176/862, baseline = 0.5554, acc = 0.6837
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 98, n_samples = 179/862, baseline = 0.5534, acc = 0.6720
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0200, intercept=True iter = 99, n_samples = 174/862, baseline = 0.5291, acc = 0.6570
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 0, n_samples = 172/862, baseline = 0.5435, acc = 0.6971
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 1, n_samples = 185/862, baseline = 0.5539, acc = 0.6913
-- [u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 2, n_samples = 176/862, baseline = 0.5510, acc = 0.6924
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 3, n_samples = 181/862, baseline = 0.5683, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 4, n_samples = 167/862, baseline = 0.5568, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 5, n_samples = 165/862, baseline = 0.5610, acc = 0.6858
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 6, n_samples = 164/862, baseline = 0.5659, acc = 0.6748
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 7, n_samples = 174/862, baseline = 0.5422, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 8, n_samples = 152/862, baseline = 0.5423, acc = 0.6915
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 9, n_samples = 148/862, baseline = 0.5504, acc = 0.6905
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 10, n_samples = 161/862, baseline = 0.5478, acc = 0.6748
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 11, n_samples = 175/862, baseline = 0.5546, acc = 0.6798
-- ['Age', u'Max D', 'Volume', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 12, n_samples = 182/862, baseline = 0.5338, acc = 0.6809
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 13, n_samples = 190/862, baseline = 0.5446, acc = 0.6860
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 14, n_samples = 151/862, baseline = 0.5429, acc = 0.6737
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 15, n_samples = 175/862, baseline = 0.5517, acc = 0.6754
-- [u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 16, n_samples = 165/862, baseline = 0.5681, acc = 0.6571
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 17, n_samples = 172/862, baseline = 0.5623, acc = 0.6986
-- [u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 18, n_samples = 179/862, baseline = 0.5549, acc = 0.6999
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 19, n_samples = 179/862, baseline = 0.5622, acc = 0.6720
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 20, n_samples = 153/862, baseline = 0.5515, acc = 0.6685
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 21, n_samples = 174/862, baseline = 0.5494, acc = 0.6817
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 22, n_samples = 194/862, baseline = 0.5599, acc = 0.6961
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 23, n_samples = 174/862, baseline = 0.5552, acc = 0.6890
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 24, n_samples = 176/862, baseline = 0.5583, acc = 0.6822
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 25, n_samples = 175/862, baseline = 0.5619, acc = 0.7162
-- [u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 26, n_samples = 185/862, baseline = 0.5465, acc = 0.6721
-- ['Age', u'SM', u'Max D', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 27, n_samples = 157/862, baseline = 0.5589, acc = 0.6709
-- ['Age', u'Max D', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 28, n_samples = 176/862, baseline = 0.5481, acc = 0.6866
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 29, n_samples = 167/862, baseline = 0.5396, acc = 0.6820
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 30, n_samples = 161/862, baseline = 0.5506, acc = 0.6862
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 31, n_samples = 167/862, baseline = 0.5525, acc = 0.6835
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 32, n_samples = 177/862, baseline = 0.5591, acc = 0.6876
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 33, n_samples = 166/862, baseline = 0.5560, acc = 0.6940
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 34, n_samples = 163/862, baseline = 0.5408, acc = 0.6867
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 35, n_samples = 157/862, baseline = 0.5461, acc = 0.6723
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 36, n_samples = 171/862, baseline = 0.5297, acc = 0.6657
-- [u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 37, n_samples = 179/862, baseline = 0.5373, acc = 0.6779
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 38, n_samples = 179/862, baseline = 0.5403, acc = 0.6837
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 39, n_samples = 184/862, baseline = 0.5413, acc = 0.6785
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 40, n_samples = 161/862, baseline = 0.5392, acc = 0.6762
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 41, n_samples = 169/862, baseline = 0.5455, acc = 0.6782
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 42, n_samples = 179/862, baseline = 0.5505, acc = 0.6896
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 43, n_samples = 189/862, baseline = 0.5290, acc = 0.6553
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 44, n_samples = 196/862, baseline = 0.5405, acc = 0.6982
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 45, n_samples = 162/862, baseline = 0.5500, acc = 0.6900
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 46, n_samples = 155/862, baseline = 0.5403, acc = 0.6662
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 47, n_samples = 195/862, baseline = 0.5547, acc = 0.6837
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 48, n_samples = 168/862, baseline = 0.5490, acc = 0.6916
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 49, n_samples = 172/862, baseline = 0.5406, acc = 0.6826
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 50, n_samples = 178/862, baseline = 0.5570, acc = 0.6754
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 51, n_samples = 182/862, baseline = 0.5515, acc = 0.6853
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 52, n_samples = 185/862, baseline = 0.5465, acc = 0.6824
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 53, n_samples = 173/862, baseline = 0.5646, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 54, n_samples = 186/862, baseline = 0.5429, acc = 0.6746
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 55, n_samples = 192/862, baseline = 0.5522, acc = 0.6791
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 56, n_samples = 185/862, baseline = 0.5672, acc = 0.6484
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 57, n_samples = 187/862, baseline = 0.5422, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 58, n_samples = 184/862, baseline = 0.5546, acc = 0.6903
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 59, n_samples = 172/862, baseline = 0.5667, acc = 0.7014
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 60, n_samples = 164/862, baseline = 0.5415, acc = 0.6877
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 61, n_samples = 169/862, baseline = 0.5527, acc = 0.6797
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LinearSVC: C = 0.0400, intercept=True iter = 62, n_samples = 174/862, baseline = 0.5480, acc = 0.6846
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 63, n_samples = 166/862, baseline = 0.5647, acc = 0.6681
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 64, n_samples = 188/862, baseline = 0.5475, acc = 0.6528
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 65, n_samples = 177/862, baseline = 0.5547, acc = 0.6891
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 66, n_samples = 180/862, baseline = 0.5425, acc = 0.6818
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 67, n_samples = 174/862, baseline = 0.5494, acc = 0.6831
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 68, n_samples = 191/862, baseline = 0.5589, acc = 0.6632
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 69, n_samples = 173/862, baseline = 0.5486, acc = 0.6749
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 70, n_samples = 173/862, baseline = 0.5573, acc = 0.6981
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 71, n_samples = 189/862, baseline = 0.5617, acc = 0.6716
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 72, n_samples = 172/862, baseline = 0.5667, acc = 0.6899
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 73, n_samples = 159/862, baseline = 0.5718, acc = 0.6871
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 74, n_samples = 156/862, baseline = 0.5425, acc = 0.6756
-- ['Age', u'Max D', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 75, n_samples = 174/862, baseline = 0.5523, acc = 0.6962
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 76, n_samples = 160/862, baseline = 0.5456, acc = 0.6994
-- [u'Max D', 'Volume', 'Max_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 77, n_samples = 165/862, baseline = 0.5610, acc = 0.6887
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 78, n_samples = 158/862, baseline = 0.5724, acc = 0.6776
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 79, n_samples = 165/862, baseline = 0.5481, acc = 0.6829
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 80, n_samples = 179/862, baseline = 0.5461, acc = 0.6911
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 81, n_samples = 156/862, baseline = 0.5453, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 82, n_samples = 186/862, baseline = 0.5518, acc = 0.7012
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 83, n_samples = 176/862, baseline = 0.5452, acc = 0.6968
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 84, n_samples = 186/862, baseline = 0.5547, acc = 0.6908
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 85, n_samples = 172/862, baseline = 0.5435, acc = 0.6710
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 86, n_samples = 182/862, baseline = 0.5618, acc = 0.6868
-- ['Age', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 87, n_samples = 153/862, baseline = 0.5430, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 88, n_samples = 176/862, baseline = 0.5496, acc = 0.7041
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 89, n_samples = 162/862, baseline = 0.5586, acc = 0.6943
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 90, n_samples = 158/862, baseline = 0.5440, acc = 0.6818
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 91, n_samples = 163/862, baseline = 0.5494, acc = 0.6838
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 92, n_samples = 184/862, baseline = 0.5575, acc = 0.6991
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 93, n_samples = 170/862, baseline = 0.5578, acc = 0.7009
-- ['Age', u'SM', u'Max D', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 94, n_samples = 179/862, baseline = 0.5461, acc = 0.6691
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 95, n_samples = 180/862, baseline = 0.5543, acc = 0.6891
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0400, intercept=True iter = 96, n_samples = 172/862, baseline = 0.5493, acc = 0.6855
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 97, n_samples = 179/862, baseline = 0.5505, acc = 0.6867
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0400, intercept=True iter = 98, n_samples = 156/862, baseline = 0.5680, acc = 0.6601
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0400, intercept=True iter = 99, n_samples = 184/862, baseline = 0.5634, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 0, n_samples = 157/862, baseline = 0.5589, acc = 0.6865
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 1, n_samples = 158/862, baseline = 0.5426, acc = 0.6932
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 2, n_samples = 169/862, baseline = 0.5469, acc = 0.6869
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 3, n_samples = 155/862, baseline = 0.5431, acc = 0.6549
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 4, n_samples = 168/862, baseline = 0.5533, acc = 0.6859
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 5, n_samples = 184/862, baseline = 0.5354, acc = 0.6740
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 6, n_samples = 161/862, baseline = 0.5407, acc = 0.6919
-- ['Age', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 7, n_samples = 166/862, baseline = 0.5517, acc = 0.7069
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 8, n_samples = 174/862, baseline = 0.5480, acc = 0.6788
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 9, n_samples = 161/862, baseline = 0.5549, acc = 0.7019
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 10, n_samples = 172/862, baseline = 0.5594, acc = 0.7014
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 11, n_samples = 175/862, baseline = 0.5633, acc = 0.6827
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 12, n_samples = 136/862, baseline = 0.5537, acc = 0.6763
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 13, n_samples = 190/862, baseline = 0.5536, acc = 0.6682
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 14, n_samples = 189/862, baseline = 0.5379, acc = 0.6895
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 15, n_samples = 161/862, baseline = 0.5521, acc = 0.6876
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 16, n_samples = 188/862, baseline = 0.5475, acc = 0.6914
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 17, n_samples = 187/862, baseline = 0.5422, acc = 0.6785
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 18, n_samples = 173/862, baseline = 0.5573, acc = 0.6894
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 19, n_samples = 170/862, baseline = 0.5592, acc = 0.6720
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 20, n_samples = 153/862, baseline = 0.5543, acc = 0.6897
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 21, n_samples = 174/862, baseline = 0.5436, acc = 0.6831
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 22, n_samples = 181/862, baseline = 0.5624, acc = 0.7078
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 23, n_samples = 161/862, baseline = 0.5535, acc = 0.6805
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 24, n_samples = 169/862, baseline = 0.5397, acc = 0.6753
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 25, n_samples = 179/862, baseline = 0.5447, acc = 0.6662
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 26, n_samples = 167/862, baseline = 0.5511, acc = 0.6777
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 27, n_samples = 178/862, baseline = 0.5526, acc = 0.7003
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 28, n_samples = 195/862, baseline = 0.5532, acc = 0.6942
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 29, n_samples = 159/862, baseline = 0.5676, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 30, n_samples = 182/862, baseline = 0.5441, acc = 0.6824
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 31, n_samples = 167/862, baseline = 0.5554, acc = 0.6748
-- [u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 32, n_samples = 180/862, baseline = 0.5499, acc = 0.6789
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 33, n_samples = 182/862, baseline = 0.5515, acc = 0.6706
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 34, n_samples = 156/862, baseline = 0.5439, acc = 0.6813
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 35, n_samples = 185/862, baseline = 0.5495, acc = 0.6928
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 36, n_samples = 198/862, baseline = 0.5617, acc = 0.6807
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 37, n_samples = 171/862, baseline = 0.5557, acc = 0.6874
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 38, n_samples = 186/862, baseline = 0.5562, acc = 0.6716
-- ['Age', u'SM', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 39, n_samples = 170/862, baseline = 0.5535, acc = 0.6879
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 40, n_samples = 171/862, baseline = 0.5398, acc = 0.6975
-- [u'Sex', 'Age', u'Max D', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 41, n_samples = 174/862, baseline = 0.5465, acc = 0.7020
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 42, n_samples = 173/862, baseline = 0.5646, acc = 0.6923
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 43, n_samples = 172/862, baseline = 0.5507, acc = 0.6812
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 44, n_samples = 161/862, baseline = 0.5407, acc = 0.6648
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 45, n_samples = 166/862, baseline = 0.5445, acc = 0.6882
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 46, n_samples = 197/862, baseline = 0.5383, acc = 0.6737
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 47, n_samples = 163/862, baseline = 0.5408, acc = 0.6896
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 48, n_samples = 190/862, baseline = 0.5327, acc = 0.6443
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 49, n_samples = 188/862, baseline = 0.5549, acc = 0.6944
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 50, n_samples = 174/862, baseline = 0.5451, acc = 0.6948
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 51, n_samples = 179/862, baseline = 0.5593, acc = 0.6589
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 52, n_samples = 168/862, baseline = 0.5562, acc = 0.6873
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 53, n_samples = 182/862, baseline = 0.5471, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 54, n_samples = 152/862, baseline = 0.5324, acc = 0.6563
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 55, n_samples = 181/862, baseline = 0.5653, acc = 0.6799
-- [u'Sex', 'Age', u'Max D', 'Volume', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 56, n_samples = 179/862, baseline = 0.5534, acc = 0.6779
-- [u'Sex', 'Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 57, n_samples = 170/862, baseline = 0.5578, acc = 0.6835
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 58, n_samples = 162/862, baseline = 0.5514, acc = 0.6743
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 59, n_samples = 171/862, baseline = 0.5557, acc = 0.6874
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 60, n_samples = 181/862, baseline = 0.5374, acc = 0.7107
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 61, n_samples = 164/862, baseline = 0.5358, acc = 0.6848
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 62, n_samples = 185/862, baseline = 0.5391, acc = 0.6987
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 63, n_samples = 176/862, baseline = 0.5481, acc = 0.6924
-- [u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 64, n_samples = 171/862, baseline = 0.5514, acc = 0.7004
-- [u'Sex', 'Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 65, n_samples = 181/862, baseline = 0.5463, acc = 0.6828
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 66, n_samples = 191/862, baseline = 0.5559, acc = 0.7049
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 67, n_samples = 161/862, baseline = 0.5449, acc = 0.6947
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 68, n_samples = 170/862, baseline = 0.5520, acc = 0.6908
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 69, n_samples = 185/862, baseline = 0.5451, acc = 0.6869
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 70, n_samples = 181/862, baseline = 0.5565, acc = 0.6960
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 71, n_samples = 188/862, baseline = 0.5415, acc = 0.6543
-- ['Age', u'SM', u'Max D', 'Volume', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 72, n_samples = 167/862, baseline = 0.5410, acc = 0.6878
-- [u'Sex', 'Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 73, n_samples = 140/862, baseline = 0.5416, acc = 0.6676
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 74, n_samples = 175/862, baseline = 0.5546, acc = 0.6856
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 75, n_samples = 176/862, baseline = 0.5379, acc = 0.6910
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 76, n_samples = 182/862, baseline = 0.5500, acc = 0.6882
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 77, n_samples = 152/862, baseline = 0.5521, acc = 0.6845
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 78, n_samples = 151/862, baseline = 0.5499, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'History_of_Hemorrhage', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 79, n_samples = 158/862, baseline = 0.5412, acc = 0.6705
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 80, n_samples = 162/862, baseline = 0.5414, acc = 0.7043
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 81, n_samples = 185/862, baseline = 0.5539, acc = 0.6839
-- [u'Sex', 'Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 82, n_samples = 151/862, baseline = 0.5584, acc = 0.6596
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 83, n_samples = 178/862, baseline = 0.5541, acc = 0.6842
-- ['Age', u'Max D', 'Volume', u'Embo', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 84, n_samples = 157/862, baseline = 0.5631, acc = 0.6894
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 85, n_samples = 167/862, baseline = 0.5309, acc = 0.6619
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 86, n_samples = 157/862, baseline = 0.5603, acc = 0.6610
-- ['Age', u'SM', u'Max D', 'History_of_Hemorrhage', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 87, n_samples = 190/862, baseline = 0.5580, acc = 0.6786
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 88, n_samples = 170/862, baseline = 0.5650, acc = 0.6893
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 89, n_samples = 158/862, baseline = 0.5412, acc = 0.6818
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 90, n_samples = 178/862, baseline = 0.5453, acc = 0.6652
-- ['Age', u'SM', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 91, n_samples = 155/862, baseline = 0.5545, acc = 0.6761
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 92, n_samples = 193/862, baseline = 0.5426, acc = 0.6876
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 93, n_samples = 170/862, baseline = 0.5491, acc = 0.6720
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 94, n_samples = 163/862, baseline = 0.5465, acc = 0.6824
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 95, n_samples = 179/862, baseline = 0.5637, acc = 0.6706
-- ['Age', u'Max D', 'Volume', u'Embo', 'Aneurysm', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 96, n_samples = 187/862, baseline = 0.5393, acc = 0.6741
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots', 'Early_RIC']
LinearSVC: C = 0.0800, intercept=True iter = 97, n_samples = 164/862, baseline = 0.5358, acc = 0.6605
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots']
LinearSVC: C = 0.0800, intercept=True iter = 98, n_samples = 189/862, baseline = 0.5483, acc = 0.6776
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LinearSVC: C = 0.0800, intercept=True iter = 99, n_samples = 188/862, baseline = 0.5475, acc = 0.6736
-- [u'Sex', 'Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0200, intercept=False iter = 0, n_samples = 350/862, baseline = 0.5508, acc = 0.6758
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 1, n_samples = 333/862, baseline = 0.5652, acc = 0.6843
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 2, n_samples = 357/862, baseline = 0.5446, acc = 0.6772
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 3, n_samples = 320/862, baseline = 0.5535, acc = 0.6845
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 4, n_samples = 349/862, baseline = 0.5341, acc = 0.6550
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 5, n_samples = 338/862, baseline = 0.5573, acc = 0.6775
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 6, n_samples = 342/862, baseline = 0.5462, acc = 0.6654
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 7, n_samples = 350/862, baseline = 0.5410, acc = 0.6895
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 8, n_samples = 324/862, baseline = 0.5781, acc = 0.6766
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 9, n_samples = 336/862, baseline = 0.5494, acc = 0.6825
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 10, n_samples = 335/862, baseline = 0.5541, acc = 0.6850
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 11, n_samples = 336/862, baseline = 0.5570, acc = 0.6749
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 12, n_samples = 342/862, baseline = 0.5596, acc = 0.6865
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 13, n_samples = 346/862, baseline = 0.5504, acc = 0.6764
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 14, n_samples = 334/862, baseline = 0.5417, acc = 0.6534
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 15, n_samples = 326/862, baseline = 0.5205, acc = 0.6586
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 16, n_samples = 364/862, baseline = 0.5542, acc = 0.7068
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 17, n_samples = 348/862, baseline = 0.5467, acc = 0.6770
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 18, n_samples = 359/862, baseline = 0.5368, acc = 0.6700
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 19, n_samples = 354/862, baseline = 0.5709, acc = 0.6673
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 20, n_samples = 367/862, baseline = 0.5616, acc = 0.6566
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 21, n_samples = 359/862, baseline = 0.5427, acc = 0.6660
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 22, n_samples = 329/862, baseline = 0.5347, acc = 0.6867
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 23, n_samples = 341/862, baseline = 0.5489, acc = 0.6641
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 24, n_samples = 357/862, baseline = 0.5505, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 25, n_samples = 355/862, baseline = 0.5325, acc = 0.6588
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 26, n_samples = 357/862, baseline = 0.5564, acc = 0.6832
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 27, n_samples = 317/862, baseline = 0.5450, acc = 0.6642
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 28, n_samples = 353/862, baseline = 0.5462, acc = 0.7073
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 29, n_samples = 353/862, baseline = 0.5658, acc = 0.6778
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 30, n_samples = 351/862, baseline = 0.5499, acc = 0.6810
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 31, n_samples = 353/862, baseline = 0.5442, acc = 0.6935
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 32, n_samples = 344/862, baseline = 0.5656, acc = 0.6467
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 33, n_samples = 335/862, baseline = 0.5503, acc = 0.6660
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 34, n_samples = 330/862, baseline = 0.5639, acc = 0.7049
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 35, n_samples = 339/862, baseline = 0.5507, acc = 0.6597
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 36, n_samples = 313/862, baseline = 0.5592, acc = 0.6721
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 37, n_samples = 373/862, baseline = 0.5501, acc = 0.7014
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 38, n_samples = 356/862, baseline = 0.5415, acc = 0.6680
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 39, n_samples = 347/862, baseline = 0.5476, acc = 0.6777
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 40, n_samples = 350/862, baseline = 0.5273, acc = 0.6797
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 41, n_samples = 352/862, baseline = 0.5392, acc = 0.6804
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 42, n_samples = 334/862, baseline = 0.5511, acc = 0.6799
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 43, n_samples = 338/862, baseline = 0.5515, acc = 0.6679
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 44, n_samples = 353/862, baseline = 0.5521, acc = 0.6640
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 45, n_samples = 354/862, baseline = 0.5551, acc = 0.6988
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 46, n_samples = 334/862, baseline = 0.5511, acc = 0.6648
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 47, n_samples = 369/862, baseline = 0.5740, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 48, n_samples = 326/862, baseline = 0.5466, acc = 0.6604
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 49, n_samples = 328/862, baseline = 0.5730, acc = 0.6648
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 50, n_samples = 352/862, baseline = 0.5392, acc = 0.6647
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 51, n_samples = 322/862, baseline = 0.5593, acc = 0.6963
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 52, n_samples = 344/862, baseline = 0.5463, acc = 0.6525
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 53, n_samples = 350/862, baseline = 0.5586, acc = 0.6758
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 54, n_samples = 329/862, baseline = 0.5460, acc = 0.6604
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 55, n_samples = 339/862, baseline = 0.5602, acc = 0.6845
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 56, n_samples = 335/862, baseline = 0.5598, acc = 0.6698
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 57, n_samples = 340/862, baseline = 0.5383, acc = 0.6839
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 58, n_samples = 359/862, baseline = 0.5527, acc = 0.6680
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 59, n_samples = 367/862, baseline = 0.5677, acc = 0.6808
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 60, n_samples = 365/862, baseline = 0.5614, acc = 0.6861
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 61, n_samples = 367/862, baseline = 0.5374, acc = 0.6505
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 62, n_samples = 342/862, baseline = 0.5423, acc = 0.6846
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 63, n_samples = 331/862, baseline = 0.5424, acc = 0.6685
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 64, n_samples = 364/862, baseline = 0.5643, acc = 0.6687
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 65, n_samples = 368/862, baseline = 0.5466, acc = 0.6943
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 66, n_samples = 364/862, baseline = 0.5402, acc = 0.6727
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 67, n_samples = 358/862, baseline = 0.5575, acc = 0.6548
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 68, n_samples = 353/862, baseline = 0.5599, acc = 0.7073
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 69, n_samples = 339/862, baseline = 0.5583, acc = 0.6788
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 70, n_samples = 361/862, baseline = 0.5928, acc = 0.6587
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 71, n_samples = 360/862, baseline = 0.5598, acc = 0.6892
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 72, n_samples = 357/862, baseline = 0.5505, acc = 0.6713
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 73, n_samples = 363/862, baseline = 0.5571, acc = 0.6713
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 74, n_samples = 351/862, baseline = 0.5460, acc = 0.6614
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 75, n_samples = 354/862, baseline = 0.5768, acc = 0.6535
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 76, n_samples = 338/862, baseline = 0.5515, acc = 0.6756
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 77, n_samples = 319/862, baseline = 0.5838, acc = 0.6225
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 78, n_samples = 337/862, baseline = 0.5543, acc = 0.6952
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 79, n_samples = 354/862, baseline = 0.5748, acc = 0.6969
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 80, n_samples = 328/862, baseline = 0.5487, acc = 0.7172
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 81, n_samples = 345/862, baseline = 0.5358, acc = 0.6615
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 82, n_samples = 340/862, baseline = 0.5402, acc = 0.6935
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 83, n_samples = 352/862, baseline = 0.5314, acc = 0.6549
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 84, n_samples = 309/862, baseline = 0.5642, acc = 0.6962
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 85, n_samples = 326/862, baseline = 0.5317, acc = 0.6586
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 86, n_samples = 321/862, baseline = 0.5601, acc = 0.6987
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 87, n_samples = 382/862, baseline = 0.5437, acc = 0.6646
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 88, n_samples = 369/862, baseline = 0.5761, acc = 0.6734
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 89, n_samples = 339/862, baseline = 0.5449, acc = 0.6711
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 90, n_samples = 341/862, baseline = 0.5605, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 91, n_samples = 371/862, baseline = 0.5743, acc = 0.6741
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 92, n_samples = 333/862, baseline = 0.5255, acc = 0.6767
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 93, n_samples = 313/862, baseline = 0.5301, acc = 0.6557
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 94, n_samples = 369/862, baseline = 0.5862, acc = 0.6694
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 95, n_samples = 345/862, baseline = 0.5435, acc = 0.6770
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 96, n_samples = 359/862, baseline = 0.5447, acc = 0.6501
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 97, n_samples = 344/862, baseline = 0.5425, acc = 0.6718
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 98, n_samples = 348/862, baseline = 0.5603, acc = 0.7101
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 99, n_samples = 344/862, baseline = 0.5483, acc = 0.6429
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 0, n_samples = 342/862, baseline = 0.5538, acc = 0.6904
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 1, n_samples = 352/862, baseline = 0.5412, acc = 0.7176
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 2, n_samples = 372/862, baseline = 0.5429, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 3, n_samples = 326/862, baseline = 0.5616, acc = 0.6660
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 4, n_samples = 355/862, baseline = 0.5483, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 5, n_samples = 331/862, baseline = 0.5669, acc = 0.6798
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 6, n_samples = 378/862, baseline = 0.5661, acc = 0.6756
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 7, n_samples = 334/862, baseline = 0.5455, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 8, n_samples = 357/862, baseline = 0.5644, acc = 0.6871
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 9, n_samples = 336/862, baseline = 0.5456, acc = 0.6920
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 10, n_samples = 353/862, baseline = 0.5383, acc = 0.6601
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 11, n_samples = 355/862, baseline = 0.5365, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 12, n_samples = 343/862, baseline = 0.5877, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 13, n_samples = 339/862, baseline = 0.5411, acc = 0.6577
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 14, n_samples = 333/862, baseline = 0.5444, acc = 0.6824
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 15, n_samples = 346/862, baseline = 0.5310, acc = 0.6589
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 16, n_samples = 360/862, baseline = 0.5618, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 17, n_samples = 313/862, baseline = 0.5792, acc = 0.6539
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 18, n_samples = 351/862, baseline = 0.5205, acc = 0.6654
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 19, n_samples = 346/862, baseline = 0.5562, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 20, n_samples = 338/862, baseline = 0.5363, acc = 0.6527
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 21, n_samples = 340/862, baseline = 0.5421, acc = 0.6839
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 22, n_samples = 344/862, baseline = 0.5405, acc = 0.6660
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 23, n_samples = 363/862, baseline = 0.5391, acc = 0.6954
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 24, n_samples = 349/862, baseline = 0.5575, acc = 0.7135
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 25, n_samples = 333/862, baseline = 0.5482, acc = 0.6541
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 26, n_samples = 344/862, baseline = 0.5714, acc = 0.6757
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 27, n_samples = 327/862, baseline = 0.5888, acc = 0.6860
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 28, n_samples = 352/862, baseline = 0.5431, acc = 0.6980
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 29, n_samples = 341/862, baseline = 0.5566, acc = 0.6795
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 30, n_samples = 377/862, baseline = 0.5505, acc = 0.7031
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 31, n_samples = 359/862, baseline = 0.5746, acc = 0.7137
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 32, n_samples = 346/862, baseline = 0.5310, acc = 0.6570
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 33, n_samples = 346/862, baseline = 0.5329, acc = 0.7112
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 34, n_samples = 329/862, baseline = 0.5366, acc = 0.6510
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 35, n_samples = 371/862, baseline = 0.5418, acc = 0.6823
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 36, n_samples = 355/862, baseline = 0.5444, acc = 0.6489
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 37, n_samples = 360/862, baseline = 0.5677, acc = 0.6713
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 38, n_samples = 362/862, baseline = 0.5340, acc = 0.6760
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 39, n_samples = 312/862, baseline = 0.5382, acc = 0.6545
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 40, n_samples = 371/862, baseline = 0.5132, acc = 0.6640
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 41, n_samples = 350/862, baseline = 0.5449, acc = 0.6641
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 42, n_samples = 332/862, baseline = 0.5623, acc = 0.6849
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 43, n_samples = 333/862, baseline = 0.5671, acc = 0.6786
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 44, n_samples = 341/862, baseline = 0.5317, acc = 0.6852
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 45, n_samples = 341/862, baseline = 0.5432, acc = 0.6622
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 46, n_samples = 340/862, baseline = 0.5556, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 47, n_samples = 340/862, baseline = 0.5536, acc = 0.6648
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 48, n_samples = 336/862, baseline = 0.5608, acc = 0.7129
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 49, n_samples = 369/862, baseline = 0.5538, acc = 0.6795
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 50, n_samples = 348/862, baseline = 0.5467, acc = 0.6829
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 51, n_samples = 352/862, baseline = 0.5608, acc = 0.6706
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 52, n_samples = 329/862, baseline = 0.5572, acc = 0.6942
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 53, n_samples = 347/862, baseline = 0.5417, acc = 0.6835
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 54, n_samples = 339/862, baseline = 0.5411, acc = 0.6501
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 55, n_samples = 328/862, baseline = 0.5524, acc = 0.6742
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 56, n_samples = 346/862, baseline = 0.5426, acc = 0.6880
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 57, n_samples = 346/862, baseline = 0.5484, acc = 0.6860
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 58, n_samples = 345/862, baseline = 0.5416, acc = 0.6925
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 59, n_samples = 311/862, baseline = 0.5517, acc = 0.6770
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 60, n_samples = 337/862, baseline = 0.5562, acc = 0.7105
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 61, n_samples = 361/862, baseline = 0.5409, acc = 0.6926
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 62, n_samples = 340/862, baseline = 0.5536, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 63, n_samples = 327/862, baseline = 0.5664, acc = 0.6879
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 64, n_samples = 358/862, baseline = 0.5575, acc = 0.6964
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 65, n_samples = 355/862, baseline = 0.5720, acc = 0.6923
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 66, n_samples = 340/862, baseline = 0.5632, acc = 0.7126
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 67, n_samples = 359/862, baseline = 0.5666, acc = 0.6640
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 68, n_samples = 335/862, baseline = 0.5541, acc = 0.6812
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 69, n_samples = 351/862, baseline = 0.5421, acc = 0.6751
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 70, n_samples = 361/862, baseline = 0.5589, acc = 0.6607
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 71, n_samples = 348/862, baseline = 0.5584, acc = 0.6907
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 72, n_samples = 336/862, baseline = 0.5494, acc = 0.6768
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 73, n_samples = 356/862, baseline = 0.5593, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 74, n_samples = 346/862, baseline = 0.5388, acc = 0.6841
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 75, n_samples = 354/862, baseline = 0.5492, acc = 0.6850
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 76, n_samples = 351/862, baseline = 0.5714, acc = 0.6869
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 77, n_samples = 333/862, baseline = 0.5425, acc = 0.6919
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 78, n_samples = 334/862, baseline = 0.5492, acc = 0.6780
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 79, n_samples = 327/862, baseline = 0.5757, acc = 0.6972
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 80, n_samples = 336/862, baseline = 0.5684, acc = 0.6920
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 81, n_samples = 351/862, baseline = 0.5734, acc = 0.6693
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 82, n_samples = 316/862, baseline = 0.5385, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 83, n_samples = 358/862, baseline = 0.5377, acc = 0.6944
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 84, n_samples = 337/862, baseline = 0.5581, acc = 0.6648
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 85, n_samples = 359/862, baseline = 0.5547, acc = 0.6561
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 86, n_samples = 334/862, baseline = 0.5322, acc = 0.6799
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 87, n_samples = 321/862, baseline = 0.5342, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 88, n_samples = 343/862, baseline = 0.5299, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 89, n_samples = 339/862, baseline = 0.5258, acc = 0.6750
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 90, n_samples = 343/862, baseline = 0.5356, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 91, n_samples = 335/862, baseline = 0.5750, acc = 0.6831
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 92, n_samples = 341/862, baseline = 0.5566, acc = 0.6929
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 93, n_samples = 360/862, baseline = 0.5518, acc = 0.6753
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 94, n_samples = 351/862, baseline = 0.5675, acc = 0.7025
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 95, n_samples = 329/862, baseline = 0.5629, acc = 0.6792
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 96, n_samples = 330/862, baseline = 0.5545, acc = 0.6936
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 97, n_samples = 353/862, baseline = 0.5246, acc = 0.6739
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 98, n_samples = 357/862, baseline = 0.5822, acc = 0.6614
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 99, n_samples = 363/862, baseline = 0.5531, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 0, n_samples = 368/862, baseline = 0.5445, acc = 0.6741
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 1, n_samples = 365/862, baseline = 0.5594, acc = 0.6821
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 2, n_samples = 368/862, baseline = 0.5506, acc = 0.6903
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 3, n_samples = 342/862, baseline = 0.5231, acc = 0.6769
-- ['Age', u'SM', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 4, n_samples = 348/862, baseline = 0.5506, acc = 0.6868
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 5, n_samples = 352/862, baseline = 0.5314, acc = 0.6824
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 6, n_samples = 324/862, baseline = 0.5539, acc = 0.6859
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 7, n_samples = 345/862, baseline = 0.5687, acc = 0.7060
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 8, n_samples = 325/862, baseline = 0.5400, acc = 0.6946
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 9, n_samples = 358/862, baseline = 0.5655, acc = 0.7044
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 10, n_samples = 369/862, baseline = 0.5598, acc = 0.7059
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 11, n_samples = 357/862, baseline = 0.5208, acc = 0.6812
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 12, n_samples = 318/862, baseline = 0.5570, acc = 0.6838
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 13, n_samples = 346/862, baseline = 0.5581, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 14, n_samples = 322/862, baseline = 0.5426, acc = 0.7000
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 15, n_samples = 339/862, baseline = 0.5468, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 16, n_samples = 375/862, baseline = 0.5708, acc = 0.6940
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 17, n_samples = 343/862, baseline = 0.5549, acc = 0.6994
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 18, n_samples = 354/862, baseline = 0.5394, acc = 0.6988
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 19, n_samples = 371/862, baseline = 0.5703, acc = 0.7067
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 20, n_samples = 345/862, baseline = 0.5493, acc = 0.6634
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 21, n_samples = 335/862, baseline = 0.5370, acc = 0.6641
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 22, n_samples = 351/862, baseline = 0.5656, acc = 0.6771
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 23, n_samples = 325/862, baseline = 0.5456, acc = 0.6760
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 24, n_samples = 337/862, baseline = 0.5486, acc = 0.7086
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 25, n_samples = 342/862, baseline = 0.5385, acc = 0.6750
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 26, n_samples = 356/862, baseline = 0.5336, acc = 0.6621
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 27, n_samples = 346/862, baseline = 0.5484, acc = 0.6880
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 28, n_samples = 356/862, baseline = 0.5395, acc = 0.6917
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 29, n_samples = 353/862, baseline = 0.5501, acc = 0.6778
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 30, n_samples = 367/862, baseline = 0.5616, acc = 0.6646
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 31, n_samples = 355/862, baseline = 0.5542, acc = 0.6943
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 32, n_samples = 354/862, baseline = 0.5413, acc = 0.6850
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 33, n_samples = 325/862, baseline = 0.5438, acc = 0.6872
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 34, n_samples = 358/862, baseline = 0.5754, acc = 0.7063
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 35, n_samples = 356/862, baseline = 0.5375, acc = 0.6937
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 36, n_samples = 346/862, baseline = 0.5504, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 37, n_samples = 361/862, baseline = 0.5589, acc = 0.6647
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 38, n_samples = 357/862, baseline = 0.5564, acc = 0.6535
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 39, n_samples = 345/862, baseline = 0.5397, acc = 0.6615
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 40, n_samples = 343/862, baseline = 0.5414, acc = 0.6744
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 41, n_samples = 324/862, baseline = 0.5706, acc = 0.7045
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 42, n_samples = 326/862, baseline = 0.5280, acc = 0.6698
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 43, n_samples = 328/862, baseline = 0.5581, acc = 0.6723
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 44, n_samples = 341/862, baseline = 0.5547, acc = 0.7083
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 45, n_samples = 363/862, baseline = 0.5311, acc = 0.6493
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 46, n_samples = 356/862, baseline = 0.5395, acc = 0.6858
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots', 'Early_RIC']
LogisticRegression: C = 0.0800, intercept=False iter = 47, n_samples = 363/862, baseline = 0.5511, acc = 0.7034
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 48, n_samples = 353/862, baseline = 0.5639, acc = 0.7053
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 49, n_samples = 340/862, baseline = 0.5192, acc = 0.6552
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 50, n_samples = 353/862, baseline = 0.5422, acc = 0.6778
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 51, n_samples = 338/862, baseline = 0.5630, acc = 0.6985
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 52, n_samples = 328/862, baseline = 0.5300, acc = 0.6517
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 53, n_samples = 349/862, baseline = 0.5595, acc = 0.7173
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 54, n_samples = 362/862, baseline = 0.5420, acc = 0.6680
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 55, n_samples = 344/862, baseline = 0.5734, acc = 0.6834
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 56, n_samples = 350/862, baseline = 0.5449, acc = 0.6973
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 57, n_samples = 329/862, baseline = 0.5553, acc = 0.6717
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 58, n_samples = 335/862, baseline = 0.5446, acc = 0.6926
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 59, n_samples = 335/862, baseline = 0.5617, acc = 0.6831
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 60, n_samples = 319/862, baseline = 0.5764, acc = 0.6777
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 61, n_samples = 344/862, baseline = 0.5502, acc = 0.6718
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 62, n_samples = 341/862, baseline = 0.5470, acc = 0.6795
-- ['Age', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 63, n_samples = 330/862, baseline = 0.5357, acc = 0.6805
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 64, n_samples = 332/862, baseline = 0.5377, acc = 0.6528
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 65, n_samples = 343/862, baseline = 0.5434, acc = 0.7052
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 66, n_samples = 344/862, baseline = 0.5502, acc = 0.6853
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 67, n_samples = 353/862, baseline = 0.5619, acc = 0.7014
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 68, n_samples = 323/862, baseline = 0.5603, acc = 0.6883
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 69, n_samples = 367/862, baseline = 0.5535, acc = 0.6626
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 70, n_samples = 351/862, baseline = 0.5597, acc = 0.6751
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 71, n_samples = 341/862, baseline = 0.5298, acc = 0.6775
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 72, n_samples = 342/862, baseline = 0.5269, acc = 0.6615
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 73, n_samples = 354/862, baseline = 0.5807, acc = 0.6949
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 74, n_samples = 350/862, baseline = 0.5449, acc = 0.6816
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 75, n_samples = 348/862, baseline = 0.5409, acc = 0.6848
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 76, n_samples = 337/862, baseline = 0.5581, acc = 0.6876
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 77, n_samples = 350/862, baseline = 0.5605, acc = 0.6680
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 78, n_samples = 347/862, baseline = 0.5476, acc = 0.6990
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 79, n_samples = 341/862, baseline = 0.5278, acc = 0.6718
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 80, n_samples = 366/862, baseline = 0.5504, acc = 0.6815
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 81, n_samples = 336/862, baseline = 0.5513, acc = 0.6863
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 82, n_samples = 341/862, baseline = 0.5566, acc = 0.6987
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 83, n_samples = 350/862, baseline = 0.5469, acc = 0.6621
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 84, n_samples = 372/862, baseline = 0.5367, acc = 0.6755
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 85, n_samples = 327/862, baseline = 0.5551, acc = 0.6804
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 86, n_samples = 367/862, baseline = 0.5455, acc = 0.6949
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 87, n_samples = 344/862, baseline = 0.5598, acc = 0.6950
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 88, n_samples = 338/862, baseline = 0.5553, acc = 0.6927
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 89, n_samples = 347/862, baseline = 0.5534, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 90, n_samples = 346/862, baseline = 0.5620, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 91, n_samples = 338/862, baseline = 0.5515, acc = 0.6851
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 92, n_samples = 343/862, baseline = 0.5626, acc = 0.6782
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 93, n_samples = 347/862, baseline = 0.5456, acc = 0.6816
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 94, n_samples = 334/862, baseline = 0.5511, acc = 0.6894
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 95, n_samples = 350/862, baseline = 0.5508, acc = 0.6895
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 96, n_samples = 379/862, baseline = 0.5714, acc = 0.6708
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 97, n_samples = 346/862, baseline = 0.5562, acc = 0.6977
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 98, n_samples = 335/862, baseline = 0.5408, acc = 0.7097
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 99, n_samples = 367/862, baseline = 0.5434, acc = 0.6929
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0200, intercept=True iter = 0, n_samples = 350/862, baseline = 0.5352, acc = 0.6777
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 1, n_samples = 352/862, baseline = 0.5314, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 2, n_samples = 331/862, baseline = 0.5273, acc = 0.6591
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 3, n_samples = 363/862, baseline = 0.5551, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 4, n_samples = 354/862, baseline = 0.5472, acc = 0.6634
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 5, n_samples = 353/862, baseline = 0.5481, acc = 0.6817
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 6, n_samples = 356/862, baseline = 0.5830, acc = 0.6838
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 7, n_samples = 360/862, baseline = 0.5359, acc = 0.6992
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 8, n_samples = 336/862, baseline = 0.5856, acc = 0.6692
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 9, n_samples = 330/862, baseline = 0.5602, acc = 0.6654
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 10, n_samples = 338/862, baseline = 0.5496, acc = 0.6794
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 11, n_samples = 340/862, baseline = 0.5632, acc = 0.6686
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 12, n_samples = 312/862, baseline = 0.5491, acc = 0.6745
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 13, n_samples = 351/862, baseline = 0.5460, acc = 0.6575
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 14, n_samples = 356/862, baseline = 0.5415, acc = 0.6739
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 15, n_samples = 356/862, baseline = 0.5395, acc = 0.6976
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 16, n_samples = 328/862, baseline = 0.5674, acc = 0.6891
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 17, n_samples = 314/862, baseline = 0.5328, acc = 0.6697
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 18, n_samples = 337/862, baseline = 0.5600, acc = 0.6743
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 19, n_samples = 332/862, baseline = 0.5358, acc = 0.6642
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 20, n_samples = 362/862, baseline = 0.5580, acc = 0.6700
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 21, n_samples = 356/862, baseline = 0.5455, acc = 0.6838
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 22, n_samples = 339/862, baseline = 0.5583, acc = 0.6941
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 23, n_samples = 342/862, baseline = 0.5462, acc = 0.6712
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 24, n_samples = 340/862, baseline = 0.5632, acc = 0.6648
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 25, n_samples = 343/862, baseline = 0.5511, acc = 0.6686
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 26, n_samples = 348/862, baseline = 0.5331, acc = 0.6732
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 27, n_samples = 351/862, baseline = 0.5597, acc = 0.6888
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 28, n_samples = 343/862, baseline = 0.5414, acc = 0.6879
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 29, n_samples = 342/862, baseline = 0.5500, acc = 0.6865
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 30, n_samples = 369/862, baseline = 0.5477, acc = 0.6856
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 31, n_samples = 330/862, baseline = 0.5564, acc = 0.6598
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 32, n_samples = 341/862, baseline = 0.5432, acc = 0.7006
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 33, n_samples = 347/862, baseline = 0.5456, acc = 0.6777
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 34, n_samples = 336/862, baseline = 0.5266, acc = 0.6692
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 35, n_samples = 337/862, baseline = 0.5295, acc = 0.6533
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 36, n_samples = 335/862, baseline = 0.5370, acc = 0.6584
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 37, n_samples = 341/862, baseline = 0.5317, acc = 0.6564
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 38, n_samples = 334/862, baseline = 0.5360, acc = 0.6705
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 39, n_samples = 331/862, baseline = 0.5499, acc = 0.6817
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 40, n_samples = 362/862, baseline = 0.5220, acc = 0.6680
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 41, n_samples = 323/862, baseline = 0.5677, acc = 0.6809
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 42, n_samples = 346/862, baseline = 0.5426, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 43, n_samples = 330/862, baseline = 0.5658, acc = 0.6767
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 44, n_samples = 332/862, baseline = 0.5547, acc = 0.6868
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 45, n_samples = 354/862, baseline = 0.5551, acc = 0.6713
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 46, n_samples = 357/862, baseline = 0.5584, acc = 0.6634
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 47, n_samples = 375/862, baseline = 0.5544, acc = 0.6591
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 48, n_samples = 374/862, baseline = 0.5717, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 49, n_samples = 374/862, baseline = 0.5963, acc = 0.6988
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 50, n_samples = 296/862, baseline = 0.5601, acc = 0.6890
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 51, n_samples = 332/862, baseline = 0.5547, acc = 0.6698
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 52, n_samples = 358/862, baseline = 0.5694, acc = 0.6587
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 53, n_samples = 331/862, baseline = 0.5706, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 54, n_samples = 324/862, baseline = 0.5335, acc = 0.6654
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 55, n_samples = 350/862, baseline = 0.5605, acc = 0.6836
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 56, n_samples = 337/862, baseline = 0.5848, acc = 0.6876
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 57, n_samples = 329/862, baseline = 0.5366, acc = 0.6754
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 58, n_samples = 358/862, baseline = 0.5397, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 59, n_samples = 344/862, baseline = 0.5483, acc = 0.6834
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 60, n_samples = 334/862, baseline = 0.5587, acc = 0.7064
-- [u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 61, n_samples = 359/862, baseline = 0.5646, acc = 0.6759
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 62, n_samples = 335/862, baseline = 0.5465, acc = 0.6622
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 63, n_samples = 339/862, baseline = 0.5870, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 64, n_samples = 371/862, baseline = 0.5682, acc = 0.7128
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 65, n_samples = 356/862, baseline = 0.5613, acc = 0.6700
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 66, n_samples = 334/862, baseline = 0.5530, acc = 0.6799
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 67, n_samples = 329/862, baseline = 0.5666, acc = 0.6773
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 68, n_samples = 335/862, baseline = 0.5712, acc = 0.6964
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 69, n_samples = 335/862, baseline = 0.5825, acc = 0.6717
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 70, n_samples = 359/862, baseline = 0.5527, acc = 0.6700
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 71, n_samples = 311/862, baseline = 0.5699, acc = 0.6715
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 72, n_samples = 360/862, baseline = 0.5498, acc = 0.7131
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 73, n_samples = 332/862, baseline = 0.5830, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 74, n_samples = 347/862, baseline = 0.5476, acc = 0.6602
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 75, n_samples = 381/862, baseline = 0.5551, acc = 0.6507
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 76, n_samples = 340/862, baseline = 0.5613, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 77, n_samples = 339/862, baseline = 0.5583, acc = 0.7055
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 78, n_samples = 361/862, baseline = 0.5509, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 79, n_samples = 327/862, baseline = 0.5402, acc = 0.6729
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 80, n_samples = 362/862, baseline = 0.5620, acc = 0.7100
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 81, n_samples = 335/862, baseline = 0.5541, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 82, n_samples = 373/862, baseline = 0.5481, acc = 0.6605
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 83, n_samples = 328/862, baseline = 0.5618, acc = 0.6816
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 84, n_samples = 343/862, baseline = 0.5202, acc = 0.6513
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 85, n_samples = 347/862, baseline = 0.5650, acc = 0.7068
-- [u'Max D', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 86, n_samples = 335/862, baseline = 0.5598, acc = 0.6717
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 87, n_samples = 325/862, baseline = 0.5624, acc = 0.6480
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 88, n_samples = 352/862, baseline = 0.5490, acc = 0.6706
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 89, n_samples = 358/862, baseline = 0.5456, acc = 0.6825
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 90, n_samples = 345/862, baseline = 0.5532, acc = 0.7060
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 91, n_samples = 334/862, baseline = 0.5473, acc = 0.6818
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 92, n_samples = 327/862, baseline = 0.5402, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 93, n_samples = 342/862, baseline = 0.5519, acc = 0.6712
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 94, n_samples = 355/862, baseline = 0.5937, acc = 0.6864
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 95, n_samples = 333/862, baseline = 0.5520, acc = 0.6767
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 96, n_samples = 354/862, baseline = 0.5177, acc = 0.6516
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 97, n_samples = 349/862, baseline = 0.5263, acc = 0.6589
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 98, n_samples = 352/862, baseline = 0.5627, acc = 0.6725
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 99, n_samples = 338/862, baseline = 0.5515, acc = 0.6775
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 0, n_samples = 353/862, baseline = 0.5442, acc = 0.6876
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 1, n_samples = 349/862, baseline = 0.5497, acc = 0.6803
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 2, n_samples = 339/862, baseline = 0.5698, acc = 0.6864
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 3, n_samples = 368/862, baseline = 0.5324, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 4, n_samples = 343/862, baseline = 0.5665, acc = 0.6859
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 5, n_samples = 367/862, baseline = 0.5556, acc = 0.7071
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 6, n_samples = 334/862, baseline = 0.5663, acc = 0.6875
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 7, n_samples = 347/862, baseline = 0.5398, acc = 0.6913
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 8, n_samples = 337/862, baseline = 0.5181, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 9, n_samples = 359/862, baseline = 0.5726, acc = 0.6859
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 10, n_samples = 345/862, baseline = 0.5687, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 11, n_samples = 347/862, baseline = 0.5456, acc = 0.6660
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 12, n_samples = 355/862, baseline = 0.5562, acc = 0.6884
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 13, n_samples = 358/862, baseline = 0.5417, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 14, n_samples = 362/862, baseline = 0.5360, acc = 0.6640
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 15, n_samples = 334/862, baseline = 0.5436, acc = 0.6837
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 16, n_samples = 333/862, baseline = 0.5709, acc = 0.6919
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 17, n_samples = 346/862, baseline = 0.5252, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 18, n_samples = 343/862, baseline = 0.5453, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 19, n_samples = 350/862, baseline = 0.5488, acc = 0.6875
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 20, n_samples = 362/862, baseline = 0.5580, acc = 0.6980
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 21, n_samples = 340/862, baseline = 0.5517, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 22, n_samples = 344/862, baseline = 0.5579, acc = 0.7066
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 23, n_samples = 355/862, baseline = 0.5444, acc = 0.6963
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 24, n_samples = 358/862, baseline = 0.5694, acc = 0.6925
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 25, n_samples = 340/862, baseline = 0.5594, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 26, n_samples = 341/862, baseline = 0.5566, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 27, n_samples = 354/862, baseline = 0.5610, acc = 0.6752
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 28, n_samples = 335/862, baseline = 0.5484, acc = 0.6717
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 29, n_samples = 353/862, baseline = 0.5226, acc = 0.6601
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 30, n_samples = 342/862, baseline = 0.5365, acc = 0.6962
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 31, n_samples = 375/862, baseline = 0.5462, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 32, n_samples = 367/862, baseline = 0.5576, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 33, n_samples = 339/862, baseline = 0.5507, acc = 0.6558
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 34, n_samples = 330/862, baseline = 0.5508, acc = 0.6786
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 35, n_samples = 349/862, baseline = 0.5419, acc = 0.6628
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 36, n_samples = 341/862, baseline = 0.5643, acc = 0.6718
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 37, n_samples = 345/862, baseline = 0.5513, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 38, n_samples = 334/862, baseline = 0.5455, acc = 0.6723
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 39, n_samples = 331/862, baseline = 0.5556, acc = 0.6874
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 40, n_samples = 364/862, baseline = 0.5241, acc = 0.6787
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 41, n_samples = 345/862, baseline = 0.5648, acc = 0.6615
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 42, n_samples = 350/862, baseline = 0.5508, acc = 0.6953
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 43, n_samples = 363/862, baseline = 0.5591, acc = 0.6794
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 44, n_samples = 338/862, baseline = 0.5401, acc = 0.6851
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 45, n_samples = 330/862, baseline = 0.5432, acc = 0.6635
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 46, n_samples = 352/862, baseline = 0.5647, acc = 0.6980
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 47, n_samples = 305/862, baseline = 0.5619, acc = 0.6912
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 48, n_samples = 360/862, baseline = 0.5319, acc = 0.6773
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 49, n_samples = 316/862, baseline = 0.5641, acc = 0.6740
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 50, n_samples = 359/862, baseline = 0.5706, acc = 0.6918
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 51, n_samples = 342/862, baseline = 0.5327, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 52, n_samples = 332/862, baseline = 0.5340, acc = 0.6792
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 53, n_samples = 340/862, baseline = 0.5460, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 54, n_samples = 338/862, baseline = 0.5611, acc = 0.7004
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 55, n_samples = 355/862, baseline = 0.5365, acc = 0.6785
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 56, n_samples = 344/862, baseline = 0.5695, acc = 0.6931
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 57, n_samples = 332/862, baseline = 0.5415, acc = 0.6604
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 58, n_samples = 363/862, baseline = 0.5451, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 59, n_samples = 322/862, baseline = 0.5426, acc = 0.6833
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 60, n_samples = 366/862, baseline = 0.5625, acc = 0.6754
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 61, n_samples = 353/862, baseline = 0.5560, acc = 0.7033
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 62, n_samples = 342/862, baseline = 0.5577, acc = 0.7173
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 63, n_samples = 341/862, baseline = 0.5393, acc = 0.6737
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 64, n_samples = 329/862, baseline = 0.5685, acc = 0.6961
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 65, n_samples = 348/862, baseline = 0.5545, acc = 0.6887
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 66, n_samples = 359/862, baseline = 0.5726, acc = 0.7058
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 67, n_samples = 318/862, baseline = 0.5533, acc = 0.6562
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 68, n_samples = 385/862, baseline = 0.5744, acc = 0.7044
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 69, n_samples = 330/862, baseline = 0.5432, acc = 0.6729
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 70, n_samples = 337/862, baseline = 0.5562, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 71, n_samples = 318/862, baseline = 0.5625, acc = 0.6599
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 72, n_samples = 340/862, baseline = 0.5383, acc = 0.6782
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 73, n_samples = 362/862, baseline = 0.5700, acc = 0.6620
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 74, n_samples = 363/862, baseline = 0.5551, acc = 0.6273
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 75, n_samples = 356/862, baseline = 0.5455, acc = 0.6917
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 76, n_samples = 337/862, baseline = 0.5486, acc = 0.6933
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 77, n_samples = 354/862, baseline = 0.5551, acc = 0.6870
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 78, n_samples = 351/862, baseline = 0.5616, acc = 0.6693
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 79, n_samples = 333/862, baseline = 0.5595, acc = 0.7013
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 80, n_samples = 323/862, baseline = 0.5492, acc = 0.6531
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 81, n_samples = 332/862, baseline = 0.5377, acc = 0.6811
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 82, n_samples = 337/862, baseline = 0.5619, acc = 0.6800
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 83, n_samples = 355/862, baseline = 0.5523, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 84, n_samples = 352/862, baseline = 0.5922, acc = 0.6882
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 85, n_samples = 332/862, baseline = 0.5472, acc = 0.6868
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 86, n_samples = 339/862, baseline = 0.5411, acc = 0.6845
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 87, n_samples = 331/862, baseline = 0.5405, acc = 0.6949
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 88, n_samples = 338/862, baseline = 0.5305, acc = 0.7061
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 89, n_samples = 330/862, baseline = 0.5526, acc = 0.6654
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 90, n_samples = 342/862, baseline = 0.5750, acc = 0.6769
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 91, n_samples = 364/862, baseline = 0.5382, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 92, n_samples = 344/862, baseline = 0.5579, acc = 0.6911
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 93, n_samples = 329/862, baseline = 0.5629, acc = 0.6961
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 94, n_samples = 338/862, baseline = 0.5229, acc = 0.6660
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 95, n_samples = 326/862, baseline = 0.5448, acc = 0.6847
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 96, n_samples = 356/862, baseline = 0.5553, acc = 0.6877
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 97, n_samples = 346/862, baseline = 0.5446, acc = 0.6570
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 98, n_samples = 360/862, baseline = 0.5518, acc = 0.7171
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 99, n_samples = 313/862, baseline = 0.5574, acc = 0.6648
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 0, n_samples = 324/862, baseline = 0.5520, acc = 0.6729
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 1, n_samples = 324/862, baseline = 0.5595, acc = 0.6840
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 2, n_samples = 330/862, baseline = 0.5470, acc = 0.6485
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 3, n_samples = 345/862, baseline = 0.5532, acc = 0.6867
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 4, n_samples = 332/862, baseline = 0.5396, acc = 0.6491
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 5, n_samples = 347/862, baseline = 0.5612, acc = 0.7165
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 6, n_samples = 356/862, baseline = 0.5593, acc = 0.6482
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 7, n_samples = 371/862, baseline = 0.5479, acc = 0.6640
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 8, n_samples = 350/862, baseline = 0.5625, acc = 0.6523
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 9, n_samples = 348/862, baseline = 0.5486, acc = 0.6965
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 10, n_samples = 356/862, baseline = 0.5534, acc = 0.7055
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 11, n_samples = 378/862, baseline = 0.5599, acc = 0.6880
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 12, n_samples = 337/862, baseline = 0.5448, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 13, n_samples = 350/862, baseline = 0.5840, acc = 0.6934
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 14, n_samples = 312/862, baseline = 0.5400, acc = 0.6836
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 15, n_samples = 354/862, baseline = 0.5453, acc = 0.7264
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 16, n_samples = 369/862, baseline = 0.5740, acc = 0.6795
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 17, n_samples = 331/862, baseline = 0.5424, acc = 0.6874
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 18, n_samples = 343/862, baseline = 0.5491, acc = 0.6821
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 19, n_samples = 360/862, baseline = 0.5578, acc = 0.7072
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 20, n_samples = 368/862, baseline = 0.5506, acc = 0.7004
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 21, n_samples = 336/862, baseline = 0.5494, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 22, n_samples = 339/862, baseline = 0.5468, acc = 0.6845
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 23, n_samples = 378/862, baseline = 0.5599, acc = 0.7025
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 24, n_samples = 364/862, baseline = 0.5643, acc = 0.6867
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 25, n_samples = 332/862, baseline = 0.5434, acc = 0.6830
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 26, n_samples = 346/862, baseline = 0.5698, acc = 0.7132
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 27, n_samples = 377/862, baseline = 0.5505, acc = 0.6866
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 28, n_samples = 339/862, baseline = 0.5335, acc = 0.6673
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 29, n_samples = 357/862, baseline = 0.5604, acc = 0.6931
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 30, n_samples = 341/862, baseline = 0.5528, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 31, n_samples = 347/862, baseline = 0.5845, acc = 0.6757
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 32, n_samples = 370/862, baseline = 0.5467, acc = 0.6870
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 33, n_samples = 344/862, baseline = 0.5502, acc = 0.6853
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 34, n_samples = 342/862, baseline = 0.5673, acc = 0.6750
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 35, n_samples = 349/862, baseline = 0.5497, acc = 0.6881
-- ['Age', u'SM', u'Max D', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 36, n_samples = 352/862, baseline = 0.5608, acc = 0.6824
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 37, n_samples = 324/862, baseline = 0.5595, acc = 0.6784
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 38, n_samples = 340/862, baseline = 0.5556, acc = 0.6743
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 39, n_samples = 354/862, baseline = 0.5236, acc = 0.6732
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 40, n_samples = 350/862, baseline = 0.5391, acc = 0.6641
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 41, n_samples = 350/862, baseline = 0.5156, acc = 0.6680
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 42, n_samples = 340/862, baseline = 0.5556, acc = 0.6513
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 43, n_samples = 349/862, baseline = 0.5692, acc = 0.6901
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 44, n_samples = 335/862, baseline = 0.5484, acc = 0.7135
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 45, n_samples = 355/862, baseline = 0.5602, acc = 0.6726
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 46, n_samples = 337/862, baseline = 0.5600, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 47, n_samples = 355/862, baseline = 0.5464, acc = 0.6923
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 48, n_samples = 334/862, baseline = 0.5720, acc = 0.7027
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 49, n_samples = 357/862, baseline = 0.5366, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 50, n_samples = 348/862, baseline = 0.5564, acc = 0.6518
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 51, n_samples = 338/862, baseline = 0.5324, acc = 0.6851
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 52, n_samples = 329/862, baseline = 0.5553, acc = 0.7129
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 53, n_samples = 342/862, baseline = 0.5442, acc = 0.6808
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 54, n_samples = 371/862, baseline = 0.5580, acc = 0.6802
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 55, n_samples = 347/862, baseline = 0.5282, acc = 0.6718
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 56, n_samples = 355/862, baseline = 0.5503, acc = 0.6884
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 57, n_samples = 343/862, baseline = 0.5356, acc = 0.6936
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 58, n_samples = 353/862, baseline = 0.5442, acc = 0.6660
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 59, n_samples = 352/862, baseline = 0.5549, acc = 0.6824
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 60, n_samples = 348/862, baseline = 0.5389, acc = 0.6790
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 61, n_samples = 338/862, baseline = 0.5515, acc = 0.6985
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 62, n_samples = 361/862, baseline = 0.5689, acc = 0.6886
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 63, n_samples = 366/862, baseline = 0.5383, acc = 0.6915
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 64, n_samples = 337/862, baseline = 0.5448, acc = 0.6762
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 65, n_samples = 347/862, baseline = 0.5631, acc = 0.6835
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 66, n_samples = 381/862, baseline = 0.5655, acc = 0.7027
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 67, n_samples = 371/862, baseline = 0.5519, acc = 0.6762
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 68, n_samples = 318/862, baseline = 0.5533, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 69, n_samples = 345/862, baseline = 0.5532, acc = 0.6654
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 70, n_samples = 333/862, baseline = 0.5312, acc = 0.6578
-- ['Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 71, n_samples = 335/862, baseline = 0.5408, acc = 0.6565
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 72, n_samples = 331/862, baseline = 0.5556, acc = 0.6855
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 73, n_samples = 339/862, baseline = 0.5315, acc = 0.6979
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 74, n_samples = 349/862, baseline = 0.5341, acc = 0.6725
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 75, n_samples = 348/862, baseline = 0.5389, acc = 0.6790
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 76, n_samples = 362/862, baseline = 0.5440, acc = 0.6780
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 77, n_samples = 326/862, baseline = 0.5560, acc = 0.7052
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 78, n_samples = 314/862, baseline = 0.5474, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 79, n_samples = 347/862, baseline = 0.5340, acc = 0.6971
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 80, n_samples = 336/862, baseline = 0.5456, acc = 0.6825
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 81, n_samples = 353/862, baseline = 0.5540, acc = 0.6758
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 82, n_samples = 327/862, baseline = 0.5626, acc = 0.6561
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 83, n_samples = 369/862, baseline = 0.5456, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 84, n_samples = 346/862, baseline = 0.5620, acc = 0.6977
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 85, n_samples = 340/862, baseline = 0.5153, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 86, n_samples = 353/862, baseline = 0.5599, acc = 0.6935
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 87, n_samples = 351/862, baseline = 0.5577, acc = 0.7006
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 88, n_samples = 377/862, baseline = 0.5629, acc = 0.6825
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 89, n_samples = 355/862, baseline = 0.5621, acc = 0.6765
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 90, n_samples = 356/862, baseline = 0.5178, acc = 0.6621
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 91, n_samples = 342/862, baseline = 0.5365, acc = 0.6673
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 92, n_samples = 337/862, baseline = 0.5390, acc = 0.6838
-- [u'Max D', u'Embo', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 93, n_samples = 326/862, baseline = 0.5578, acc = 0.7015
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 94, n_samples = 314/862, baseline = 0.5438, acc = 0.6916
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 95, n_samples = 370/862, baseline = 0.5386, acc = 0.6768
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 96, n_samples = 332/862, baseline = 0.5642, acc = 0.6868
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 97, n_samples = 354/862, baseline = 0.5630, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 98, n_samples = 336/862, baseline = 0.5589, acc = 0.6749
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 99, n_samples = 320/862, baseline = 0.5461, acc = 0.6679
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0200, intercept=False iter = 0, n_samples = 263/862, baseline = 0.5509, acc = 0.6878
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 1, n_samples = 276/862, baseline = 0.5495, acc = 0.6724
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 2, n_samples = 267/862, baseline = 0.5496, acc = 0.6739
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 3, n_samples = 240/862, baseline = 0.5643, acc = 0.6961
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 4, n_samples = 253/862, baseline = 0.5501, acc = 0.6897
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 5, n_samples = 267/862, baseline = 0.5546, acc = 0.6588
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 6, n_samples = 263/862, baseline = 0.5576, acc = 0.6578
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 7, n_samples = 269/862, baseline = 0.5413, acc = 0.6712
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 8, n_samples = 261/862, baseline = 0.5458, acc = 0.6606
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 9, n_samples = 249/862, baseline = 0.5334, acc = 0.6721
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 10, n_samples = 260/862, baseline = 0.5498, acc = 0.6860
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 11, n_samples = 261/862, baseline = 0.5541, acc = 0.6589
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 12, n_samples = 249/862, baseline = 0.5302, acc = 0.6737
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 13, n_samples = 241/862, baseline = 0.5443, acc = 0.6763
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 14, n_samples = 281/862, baseline = 0.5525, acc = 0.6695
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 15, n_samples = 251/862, baseline = 0.5532, acc = 0.6939
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 16, n_samples = 281/862, baseline = 0.5525, acc = 0.6678
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 17, n_samples = 262/862, baseline = 0.5550, acc = 0.6917
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 18, n_samples = 279/862, baseline = 0.5489, acc = 0.6947
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 19, n_samples = 255/862, baseline = 0.5470, acc = 0.6722
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 20, n_samples = 240/862, baseline = 0.5370, acc = 0.6592
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 21, n_samples = 263/862, baseline = 0.5409, acc = 0.6628
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 22, n_samples = 270/862, baseline = 0.5253, acc = 0.6537
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 23, n_samples = 260/862, baseline = 0.5515, acc = 0.6860
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 24, n_samples = 284/862, baseline = 0.5675, acc = 0.6678
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 25, n_samples = 265/862, baseline = 0.5645, acc = 0.6717
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 26, n_samples = 272/862, baseline = 0.5542, acc = 0.6983
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 27, n_samples = 243/862, baseline = 0.5444, acc = 0.6753
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 28, n_samples = 259/862, baseline = 0.5439, acc = 0.6766
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 29, n_samples = 293/862, baseline = 0.5641, acc = 0.6766
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 30, n_samples = 245/862, baseline = 0.5511, acc = 0.6467
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 31, n_samples = 252/862, baseline = 0.5525, acc = 0.6918
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 32, n_samples = 253/862, baseline = 0.5402, acc = 0.6880
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 33, n_samples = 254/862, baseline = 0.5592, acc = 0.6678
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 34, n_samples = 269/862, baseline = 0.5599, acc = 0.6914
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 35, n_samples = 257/862, baseline = 0.5587, acc = 0.6793
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 36, n_samples = 270/862, baseline = 0.5439, acc = 0.6824
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 37, n_samples = 260/862, baseline = 0.5532, acc = 0.6927
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 38, n_samples = 258/862, baseline = 0.5281, acc = 0.6705
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 39, n_samples = 249/862, baseline = 0.5563, acc = 0.6803
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 40, n_samples = 262/862, baseline = 0.5533, acc = 0.6900
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 41, n_samples = 252/862, baseline = 0.5590, acc = 0.7066
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 42, n_samples = 262/862, baseline = 0.5317, acc = 0.6800
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 43, n_samples = 235/862, baseline = 0.5455, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 44, n_samples = 267/862, baseline = 0.5546, acc = 0.6824
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 45, n_samples = 259/862, baseline = 0.5672, acc = 0.6733
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 46, n_samples = 242/862, baseline = 0.5387, acc = 0.6774
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 47, n_samples = 268/862, baseline = 0.5842, acc = 0.6869
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 48, n_samples = 256/862, baseline = 0.5627, acc = 0.6584
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 49, n_samples = 261/862, baseline = 0.5541, acc = 0.6822
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 50, n_samples = 247/862, baseline = 0.5545, acc = 0.6780
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 51, n_samples = 305/862, baseline = 0.5601, acc = 0.6804
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 52, n_samples = 260/862, baseline = 0.5598, acc = 0.6595
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 53, n_samples = 249/862, baseline = 0.5465, acc = 0.6493
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 54, n_samples = 257/862, baseline = 0.5554, acc = 0.6727
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 55, n_samples = 256/862, baseline = 0.5495, acc = 0.6815
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 56, n_samples = 260/862, baseline = 0.5382, acc = 0.6761
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 57, n_samples = 267/862, baseline = 0.5647, acc = 0.6908
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 58, n_samples = 257/862, baseline = 0.5570, acc = 0.6876
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 59, n_samples = 234/862, baseline = 0.5382, acc = 0.6640
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 60, n_samples = 272/862, baseline = 0.5576, acc = 0.6847
-- [u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 61, n_samples = 251/862, baseline = 0.5516, acc = 0.6710
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 62, n_samples = 266/862, baseline = 0.5570, acc = 0.6913
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 63, n_samples = 283/862, baseline = 0.5665, acc = 0.6874
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 64, n_samples = 250/862, baseline = 0.5605, acc = 0.6634
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 65, n_samples = 257/862, baseline = 0.5570, acc = 0.6826
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 66, n_samples = 258/862, baseline = 0.5430, acc = 0.6474
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 67, n_samples = 279/862, baseline = 0.5678, acc = 0.6861
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 68, n_samples = 240/862, baseline = 0.5514, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 69, n_samples = 253/862, baseline = 0.5484, acc = 0.6831
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 70, n_samples = 236/862, baseline = 0.5479, acc = 0.6677
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 71, n_samples = 267/862, baseline = 0.5294, acc = 0.6756
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 72, n_samples = 246/862, baseline = 0.5747, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 73, n_samples = 250/862, baseline = 0.5408, acc = 0.6797
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 74, n_samples = 246/862, baseline = 0.5373, acc = 0.6623
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 75, n_samples = 229/862, baseline = 0.5513, acc = 0.6682
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 76, n_samples = 242/862, baseline = 0.5435, acc = 0.6677
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 77, n_samples = 254/862, baseline = 0.5329, acc = 0.6727
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 78, n_samples = 267/862, baseline = 0.5546, acc = 0.6689
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 79, n_samples = 245/862, baseline = 0.5446, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 80, n_samples = 272/862, baseline = 0.5678, acc = 0.6932
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 81, n_samples = 264/862, baseline = 0.5435, acc = 0.6890
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 82, n_samples = 269/862, baseline = 0.5413, acc = 0.6847
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 83, n_samples = 251/862, baseline = 0.5548, acc = 0.6825
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 84, n_samples = 235/862, baseline = 0.5486, acc = 0.6635
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 85, n_samples = 266/862, baseline = 0.5336, acc = 0.6846
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 86, n_samples = 270/862, baseline = 0.5659, acc = 0.7027
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 87, n_samples = 244/862, baseline = 0.5437, acc = 0.7039
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 88, n_samples = 253/862, baseline = 0.5550, acc = 0.6716
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 89, n_samples = 244/862, baseline = 0.5566, acc = 0.6764
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 90, n_samples = 285/862, baseline = 0.5529, acc = 0.6794
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 91, n_samples = 256/862, baseline = 0.5528, acc = 0.6832
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 92, n_samples = 275/862, baseline = 0.5554, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 93, n_samples = 274/862, baseline = 0.5476, acc = 0.6650
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 94, n_samples = 256/862, baseline = 0.5429, acc = 0.6766
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 95, n_samples = 258/862, baseline = 0.5679, acc = 0.6424
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 96, n_samples = 252/862, baseline = 0.5492, acc = 0.6672
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 97, n_samples = 273/862, baseline = 0.5297, acc = 0.6655
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 98, n_samples = 260/862, baseline = 0.5664, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 99, n_samples = 269/862, baseline = 0.5447, acc = 0.6678
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 0, n_samples = 269/862, baseline = 0.5514, acc = 0.6948
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 1, n_samples = 236/862, baseline = 0.5479, acc = 0.6885
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 2, n_samples = 243/862, baseline = 0.5331, acc = 0.6575
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 3, n_samples = 261/862, baseline = 0.5491, acc = 0.6606
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 4, n_samples = 241/862, baseline = 0.5572, acc = 0.6715
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 5, n_samples = 248/862, baseline = 0.5570, acc = 0.6743
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 6, n_samples = 260/862, baseline = 0.5648, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 7, n_samples = 265/862, baseline = 0.5377, acc = 0.6717
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 8, n_samples = 252/862, baseline = 0.5689, acc = 0.6721
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 9, n_samples = 241/862, baseline = 0.5572, acc = 0.6538
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 10, n_samples = 253/862, baseline = 0.5517, acc = 0.6634
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 11, n_samples = 269/862, baseline = 0.5565, acc = 0.6863
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 12, n_samples = 269/862, baseline = 0.5599, acc = 0.6594
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 13, n_samples = 279/862, baseline = 0.5334, acc = 0.6707
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 14, n_samples = 264/862, baseline = 0.5619, acc = 0.6940
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 15, n_samples = 277/862, baseline = 0.5265, acc = 0.6650
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 16, n_samples = 237/862, baseline = 0.5568, acc = 0.6720
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 17, n_samples = 253/862, baseline = 0.5599, acc = 0.6962
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 18, n_samples = 250/862, baseline = 0.5654, acc = 0.6732
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 19, n_samples = 264/862, baseline = 0.5552, acc = 0.6756
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 20, n_samples = 262/862, baseline = 0.5567, acc = 0.6967
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 21, n_samples = 229/862, baseline = 0.5624, acc = 0.6967
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 22, n_samples = 239/862, baseline = 0.5506, acc = 0.6726
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 23, n_samples = 278/862, baseline = 0.5325, acc = 0.6644
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 24, n_samples = 254/862, baseline = 0.5411, acc = 0.6612
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 25, n_samples = 262/862, baseline = 0.5450, acc = 0.6650
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 26, n_samples = 286/862, baseline = 0.5469, acc = 0.6719
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 27, n_samples = 272/862, baseline = 0.5525, acc = 0.6831
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 28, n_samples = 255/862, baseline = 0.5700, acc = 0.6755
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 29, n_samples = 270/862, baseline = 0.5541, acc = 0.6993
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 30, n_samples = 253/862, baseline = 0.5419, acc = 0.6782
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 31, n_samples = 251/862, baseline = 0.5466, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 32, n_samples = 247/862, baseline = 0.5447, acc = 0.6748
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 33, n_samples = 234/862, baseline = 0.5557, acc = 0.6799
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 34, n_samples = 248/862, baseline = 0.5423, acc = 0.6857
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 35, n_samples = 234/862, baseline = 0.5621, acc = 0.6799
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 36, n_samples = 257/862, baseline = 0.5455, acc = 0.6612
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 37, n_samples = 236/862, baseline = 0.5559, acc = 0.6869
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 38, n_samples = 235/862, baseline = 0.5375, acc = 0.6730
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 39, n_samples = 266/862, baseline = 0.5201, acc = 0.6745
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 40, n_samples = 269/862, baseline = 0.5514, acc = 0.6931
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 41, n_samples = 272/862, baseline = 0.5559, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 42, n_samples = 264/862, baseline = 0.5452, acc = 0.6873
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 43, n_samples = 251/862, baseline = 0.5499, acc = 0.6956
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 44, n_samples = 262/862, baseline = 0.5533, acc = 0.6767
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 45, n_samples = 237/862, baseline = 0.5488, acc = 0.6784
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 46, n_samples = 254/862, baseline = 0.5428, acc = 0.6809
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 47, n_samples = 255/862, baseline = 0.5437, acc = 0.6689
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 48, n_samples = 248/862, baseline = 0.5472, acc = 0.6482
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 49, n_samples = 250/862, baseline = 0.5605, acc = 0.6569
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 50, n_samples = 248/862, baseline = 0.5537, acc = 0.6775
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 51, n_samples = 252/862, baseline = 0.5393, acc = 0.6639
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 52, n_samples = 258/862, baseline = 0.5397, acc = 0.6556
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 53, n_samples = 271/862, baseline = 0.5448, acc = 0.6853
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 54, n_samples = 246/862, baseline = 0.5584, acc = 0.6948
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 55, n_samples = 274/862, baseline = 0.5527, acc = 0.6905
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 56, n_samples = 279/862, baseline = 0.5489, acc = 0.6775
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 57, n_samples = 266/862, baseline = 0.5587, acc = 0.7097
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 58, n_samples = 276/862, baseline = 0.5495, acc = 0.6911
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 59, n_samples = 267/862, baseline = 0.5462, acc = 0.6790
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 60, n_samples = 272/862, baseline = 0.5508, acc = 0.6593
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 61, n_samples = 257/862, baseline = 0.5603, acc = 0.6777
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 62, n_samples = 255/862, baseline = 0.5486, acc = 0.6969
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 63, n_samples = 251/862, baseline = 0.5516, acc = 0.6727
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 64, n_samples = 229/862, baseline = 0.5450, acc = 0.6682
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 65, n_samples = 260/862, baseline = 0.5498, acc = 0.6960
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 66, n_samples = 259/862, baseline = 0.5323, acc = 0.6716
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 67, n_samples = 262/862, baseline = 0.5433, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 68, n_samples = 257/862, baseline = 0.5455, acc = 0.6893
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 69, n_samples = 258/862, baseline = 0.5662, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 70, n_samples = 242/862, baseline = 0.5548, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 71, n_samples = 255/862, baseline = 0.5305, acc = 0.6722
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 72, n_samples = 250/862, baseline = 0.5474, acc = 0.6977
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 73, n_samples = 264/862, baseline = 0.5635, acc = 0.6823
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 74, n_samples = 259/862, baseline = 0.5506, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 75, n_samples = 258/862, baseline = 0.5596, acc = 0.6772
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 76, n_samples = 251/862, baseline = 0.5303, acc = 0.6678
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 77, n_samples = 255/862, baseline = 0.5437, acc = 0.6771
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 78, n_samples = 276/862, baseline = 0.5666, acc = 0.6962
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 79, n_samples = 255/862, baseline = 0.5552, acc = 0.6804
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 80, n_samples = 248/862, baseline = 0.5456, acc = 0.6694
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 81, n_samples = 268/862, baseline = 0.5455, acc = 0.6818
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 82, n_samples = 253/862, baseline = 0.5534, acc = 0.6782
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 83, n_samples = 253/862, baseline = 0.5583, acc = 0.6585
-- [u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 84, n_samples = 254/862, baseline = 0.5444, acc = 0.6842
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 85, n_samples = 251/862, baseline = 0.5303, acc = 0.6678
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 86, n_samples = 249/862, baseline = 0.5481, acc = 0.6754
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 87, n_samples = 270/862, baseline = 0.5591, acc = 0.6571
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 88, n_samples = 261/862, baseline = 0.5391, acc = 0.6822
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 89, n_samples = 273/862, baseline = 0.5501, acc = 0.6689
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 90, n_samples = 276/862, baseline = 0.5529, acc = 0.6621
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 91, n_samples = 271/862, baseline = 0.5584, acc = 0.6887
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 92, n_samples = 253/862, baseline = 0.5484, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 93, n_samples = 256/862, baseline = 0.5264, acc = 0.6518
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 94, n_samples = 281/862, baseline = 0.5663, acc = 0.6954
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 95, n_samples = 262/862, baseline = 0.5567, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 96, n_samples = 252/862, baseline = 0.5525, acc = 0.6902
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 97, n_samples = 269/862, baseline = 0.5497, acc = 0.6678
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 98, n_samples = 260/862, baseline = 0.5399, acc = 0.6827
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 99, n_samples = 244/862, baseline = 0.5712, acc = 0.6990
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 0, n_samples = 273/862, baseline = 0.5501, acc = 0.6723
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 1, n_samples = 268/862, baseline = 0.5640, acc = 0.6835
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 2, n_samples = 266/862, baseline = 0.5537, acc = 0.6795
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 3, n_samples = 243/862, baseline = 0.5250, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 4, n_samples = 254/862, baseline = 0.5576, acc = 0.6809
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 5, n_samples = 225/862, baseline = 0.5479, acc = 0.6876
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 6, n_samples = 255/862, baseline = 0.5552, acc = 0.6771
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 7, n_samples = 264/862, baseline = 0.5602, acc = 0.6756
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 8, n_samples = 253/862, baseline = 0.5419, acc = 0.6765
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 9, n_samples = 253/862, baseline = 0.5452, acc = 0.6601
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 10, n_samples = 238/862, baseline = 0.5401, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 11, n_samples = 269/862, baseline = 0.5599, acc = 0.7032
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 12, n_samples = 273/862, baseline = 0.5620, acc = 0.6842
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 13, n_samples = 259/862, baseline = 0.5721, acc = 0.6716
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 14, n_samples = 269/862, baseline = 0.5396, acc = 0.6948
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 15, n_samples = 265/862, baseline = 0.5611, acc = 0.6901
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 16, n_samples = 255/862, baseline = 0.5552, acc = 0.6820
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 17, n_samples = 229/862, baseline = 0.5450, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 18, n_samples = 238/862, baseline = 0.5561, acc = 0.6731
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 19, n_samples = 232/862, baseline = 0.5444, acc = 0.6905
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 20, n_samples = 259/862, baseline = 0.5522, acc = 0.6716
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 21, n_samples = 281/862, baseline = 0.5370, acc = 0.6730
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 22, n_samples = 257/862, baseline = 0.5587, acc = 0.6678
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 23, n_samples = 255/862, baseline = 0.5667, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 24, n_samples = 260/862, baseline = 0.5432, acc = 0.6827
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 25, n_samples = 275/862, baseline = 0.5451, acc = 0.6848
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 26, n_samples = 257/862, baseline = 0.5355, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 27, n_samples = 282/862, baseline = 0.5552, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots', 'Early_RIC']
LogisticRegression: C = 0.0800, intercept=False iter = 28, n_samples = 267/862, baseline = 0.5546, acc = 0.6924
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 29, n_samples = 234/862, baseline = 0.5557, acc = 0.6799
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 30, n_samples = 253/862, baseline = 0.5271, acc = 0.6831
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 31, n_samples = 254/862, baseline = 0.5609, acc = 0.6826
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 32, n_samples = 272/862, baseline = 0.5797, acc = 0.6881
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 33, n_samples = 268/862, baseline = 0.5758, acc = 0.6902
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 34, n_samples = 235/862, baseline = 0.5550, acc = 0.6746
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 35, n_samples = 258/862, baseline = 0.5381, acc = 0.6738
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 36, n_samples = 272/862, baseline = 0.5525, acc = 0.6966
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 37, n_samples = 251/862, baseline = 0.5532, acc = 0.7087
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 38, n_samples = 290/862, baseline = 0.5752, acc = 0.6888
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 39, n_samples = 262/862, baseline = 0.5533, acc = 0.6750
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 40, n_samples = 275/862, baseline = 0.5724, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 41, n_samples = 273/862, baseline = 0.5416, acc = 0.6825
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 42, n_samples = 240/862, baseline = 0.5643, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 43, n_samples = 246/862, baseline = 0.5455, acc = 0.6818
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 44, n_samples = 251/862, baseline = 0.5597, acc = 0.6759
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 45, n_samples = 238/862, baseline = 0.5609, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 46, n_samples = 245/862, baseline = 0.5575, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 47, n_samples = 260/862, baseline = 0.5382, acc = 0.6744
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 48, n_samples = 262/862, baseline = 0.5517, acc = 0.7033
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 49, n_samples = 224/862, baseline = 0.5611, acc = 0.6803
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 50, n_samples = 247/862, baseline = 0.5724, acc = 0.6618
-- ['Age', u'Max D', 'Volume', 'Aneurysm', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 51, n_samples = 255/862, baseline = 0.5255, acc = 0.6787
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 52, n_samples = 251/862, baseline = 0.5417, acc = 0.6596
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 53, n_samples = 243/862, baseline = 0.5315, acc = 0.6591
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 54, n_samples = 265/862, baseline = 0.5477, acc = 0.6817
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 55, n_samples = 259/862, baseline = 0.5423, acc = 0.6833
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 56, n_samples = 255/862, baseline = 0.5470, acc = 0.6804
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 57, n_samples = 243/862, baseline = 0.5590, acc = 0.6737
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 58, n_samples = 246/862, baseline = 0.5552, acc = 0.6818
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 59, n_samples = 239/862, baseline = 0.5425, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 60, n_samples = 263/862, baseline = 0.5626, acc = 0.6644
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 61, n_samples = 263/862, baseline = 0.5559, acc = 0.7028
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 62, n_samples = 238/862, baseline = 0.5577, acc = 0.6683
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 63, n_samples = 244/862, baseline = 0.5583, acc = 0.6780
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 64, n_samples = 261/862, baseline = 0.5474, acc = 0.6739
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 65, n_samples = 229/862, baseline = 0.5513, acc = 0.6698
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 66, n_samples = 248/862, baseline = 0.5537, acc = 0.6906
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 67, n_samples = 272/862, baseline = 0.5492, acc = 0.6780
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 68, n_samples = 261/862, baseline = 0.5707, acc = 0.7005
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 69, n_samples = 261/862, baseline = 0.5408, acc = 0.6639
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 70, n_samples = 272/862, baseline = 0.5271, acc = 0.6729
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 71, n_samples = 241/862, baseline = 0.5604, acc = 0.6570
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 72, n_samples = 253/862, baseline = 0.5222, acc = 0.6535
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 73, n_samples = 262/862, baseline = 0.5600, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 74, n_samples = 260/862, baseline = 0.5532, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 75, n_samples = 253/862, baseline = 0.5468, acc = 0.6749
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 76, n_samples = 247/862, baseline = 0.5528, acc = 0.6862
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 77, n_samples = 248/862, baseline = 0.5472, acc = 0.6889
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 78, n_samples = 233/862, baseline = 0.5548, acc = 0.7043
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 79, n_samples = 260/862, baseline = 0.5515, acc = 0.7027
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 80, n_samples = 252/862, baseline = 0.5426, acc = 0.6852
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 81, n_samples = 246/862, baseline = 0.5471, acc = 0.6981
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 82, n_samples = 246/862, baseline = 0.5422, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 83, n_samples = 254/862, baseline = 0.5625, acc = 0.6678
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 84, n_samples = 278/862, baseline = 0.5308, acc = 0.6507
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 85, n_samples = 268/862, baseline = 0.5589, acc = 0.6751
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 86, n_samples = 234/862, baseline = 0.5446, acc = 0.6736
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 87, n_samples = 263/862, baseline = 0.5392, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 88, n_samples = 295/862, baseline = 0.5414, acc = 0.6861
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 89, n_samples = 278/862, baseline = 0.5394, acc = 0.6764
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 90, n_samples = 242/862, baseline = 0.5661, acc = 0.6984
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 91, n_samples = 247/862, baseline = 0.5496, acc = 0.7073
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 92, n_samples = 264/862, baseline = 0.5485, acc = 0.6957
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 93, n_samples = 262/862, baseline = 0.5567, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 94, n_samples = 279/862, baseline = 0.5455, acc = 0.6621
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 95, n_samples = 237/862, baseline = 0.5328, acc = 0.6864
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 96, n_samples = 268/862, baseline = 0.5354, acc = 0.6700
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 97, n_samples = 245/862, baseline = 0.5559, acc = 0.6985
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 98, n_samples = 267/862, baseline = 0.5647, acc = 0.6992
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 99, n_samples = 261/862, baseline = 0.5324, acc = 0.6739
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 0, n_samples = 284/862, baseline = 0.5606, acc = 0.6903
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 1, n_samples = 258/862, baseline = 0.5447, acc = 0.6772
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 2, n_samples = 275/862, baseline = 0.5486, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 3, n_samples = 283/862, baseline = 0.5354, acc = 0.6615
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 4, n_samples = 265/862, baseline = 0.5327, acc = 0.6834
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 5, n_samples = 259/862, baseline = 0.5589, acc = 0.6551
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 6, n_samples = 264/862, baseline = 0.5635, acc = 0.6856
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 7, n_samples = 282/862, baseline = 0.5655, acc = 0.6724
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 8, n_samples = 261/862, baseline = 0.5524, acc = 0.7022
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 9, n_samples = 264/862, baseline = 0.5401, acc = 0.6940
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 10, n_samples = 247/862, baseline = 0.5528, acc = 0.6618
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 11, n_samples = 236/862, baseline = 0.5479, acc = 0.6725
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 12, n_samples = 268/862, baseline = 0.5556, acc = 0.6785
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 13, n_samples = 265/862, baseline = 0.5360, acc = 0.6683
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 14, n_samples = 262/862, baseline = 0.5417, acc = 0.6617
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 15, n_samples = 277/862, baseline = 0.5470, acc = 0.6786
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 16, n_samples = 260/862, baseline = 0.5465, acc = 0.6611
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 17, n_samples = 258/862, baseline = 0.5248, acc = 0.6623
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 18, n_samples = 265/862, baseline = 0.5394, acc = 0.6600
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 19, n_samples = 247/862, baseline = 0.5350, acc = 0.6732
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 20, n_samples = 244/862, baseline = 0.5469, acc = 0.6715
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 21, n_samples = 277/862, baseline = 0.5504, acc = 0.6974
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 22, n_samples = 274/862, baseline = 0.5493, acc = 0.6803
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 23, n_samples = 279/862, baseline = 0.5403, acc = 0.6913
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 24, n_samples = 258/862, baseline = 0.5414, acc = 0.6871
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 25, n_samples = 246/862, baseline = 0.5601, acc = 0.6899
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 26, n_samples = 262/862, baseline = 0.5517, acc = 0.6733
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 27, n_samples = 269/862, baseline = 0.5413, acc = 0.6863
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 28, n_samples = 237/862, baseline = 0.5632, acc = 0.6464
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 29, n_samples = 259/862, baseline = 0.5506, acc = 0.6716
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 30, n_samples = 280/862, baseline = 0.5189, acc = 0.6667
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 31, n_samples = 249/862, baseline = 0.5481, acc = 0.6639
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 32, n_samples = 242/862, baseline = 0.5694, acc = 0.6806
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 33, n_samples = 269/862, baseline = 0.5514, acc = 0.6695
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 34, n_samples = 271/862, baseline = 0.5499, acc = 0.6802
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 35, n_samples = 254/862, baseline = 0.5477, acc = 0.6776
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 36, n_samples = 289/862, baseline = 0.5532, acc = 0.6754
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 37, n_samples = 237/862, baseline = 0.5584, acc = 0.6672
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 38, n_samples = 241/862, baseline = 0.5491, acc = 0.6860
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 39, n_samples = 259/862, baseline = 0.5672, acc = 0.6932
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 40, n_samples = 262/862, baseline = 0.5250, acc = 0.6650
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 41, n_samples = 238/862, baseline = 0.5689, acc = 0.6875
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 42, n_samples = 241/862, baseline = 0.5539, acc = 0.6634
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 43, n_samples = 274/862, baseline = 0.5391, acc = 0.6752
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 44, n_samples = 269/862, baseline = 0.5531, acc = 0.6796
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 45, n_samples = 246/862, baseline = 0.5568, acc = 0.6786
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 46, n_samples = 241/862, baseline = 0.5443, acc = 0.6618
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 47, n_samples = 242/862, baseline = 0.5532, acc = 0.6645
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 48, n_samples = 253/862, baseline = 0.5501, acc = 0.6634
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 49, n_samples = 260/862, baseline = 0.5532, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 50, n_samples = 249/862, baseline = 0.5759, acc = 0.6574
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 51, n_samples = 252/862, baseline = 0.5492, acc = 0.6738
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 52, n_samples = 273/862, baseline = 0.5467, acc = 0.6655
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 53, n_samples = 269/862, baseline = 0.5632, acc = 0.6948
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 54, n_samples = 274/862, baseline = 0.5527, acc = 0.6633
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 55, n_samples = 226/862, baseline = 0.5597, acc = 0.6950
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 56, n_samples = 255/862, baseline = 0.5601, acc = 0.6689
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 57, n_samples = 260/862, baseline = 0.5598, acc = 0.6661
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 58, n_samples = 248/862, baseline = 0.5472, acc = 0.6596
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 59, n_samples = 274/862, baseline = 0.5442, acc = 0.6803
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0200, intercept=True iter = 60, n_samples = 243/862, baseline = 0.5428, acc = 0.6834
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 61, n_samples = 254/862, baseline = 0.5510, acc = 0.6793
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 62, n_samples = 235/862, baseline = 0.5598, acc = 0.6730
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 63, n_samples = 255/862, baseline = 0.5667, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 64, n_samples = 265/862, baseline = 0.5595, acc = 0.6516
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 65, n_samples = 234/862, baseline = 0.5525, acc = 0.6831
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 66, n_samples = 242/862, baseline = 0.5645, acc = 0.6855
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 67, n_samples = 263/862, baseline = 0.5326, acc = 0.6761
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 68, n_samples = 245/862, baseline = 0.5511, acc = 0.6645
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 69, n_samples = 273/862, baseline = 0.5501, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 70, n_samples = 252/862, baseline = 0.5639, acc = 0.6623
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 71, n_samples = 262/862, baseline = 0.5600, acc = 0.6583
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 72, n_samples = 238/862, baseline = 0.5593, acc = 0.6875
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 73, n_samples = 278/862, baseline = 0.5445, acc = 0.6918
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 74, n_samples = 250/862, baseline = 0.5637, acc = 0.6928
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 75, n_samples = 267/862, baseline = 0.5563, acc = 0.6454
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 76, n_samples = 252/862, baseline = 0.5459, acc = 0.6852
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 77, n_samples = 267/862, baseline = 0.5462, acc = 0.6319
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 78, n_samples = 276/862, baseline = 0.5751, acc = 0.6741
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 79, n_samples = 262/862, baseline = 0.5683, acc = 0.6833
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 80, n_samples = 249/862, baseline = 0.5334, acc = 0.6623
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 81, n_samples = 245/862, baseline = 0.5478, acc = 0.6742
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 82, n_samples = 258/862, baseline = 0.5679, acc = 0.6606
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 83, n_samples = 240/862, baseline = 0.5756, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 84, n_samples = 260/862, baseline = 0.5415, acc = 0.6777
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 85, n_samples = 267/862, baseline = 0.5529, acc = 0.6605
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 86, n_samples = 258/862, baseline = 0.5513, acc = 0.6672
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 87, n_samples = 259/862, baseline = 0.5539, acc = 0.6816
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 88, n_samples = 263/862, baseline = 0.5609, acc = 0.6644
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 89, n_samples = 255/862, baseline = 0.5371, acc = 0.6623
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 90, n_samples = 267/862, baseline = 0.5462, acc = 0.6790
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 91, n_samples = 258/862, baseline = 0.5364, acc = 0.6689
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 92, n_samples = 263/862, baseline = 0.5559, acc = 0.6878
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 93, n_samples = 241/862, baseline = 0.5475, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 94, n_samples = 247/862, baseline = 0.5593, acc = 0.6748
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 95, n_samples = 258/862, baseline = 0.5397, acc = 0.6887
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 96, n_samples = 234/862, baseline = 0.5525, acc = 0.7006
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 97, n_samples = 269/862, baseline = 0.5379, acc = 0.6678
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 98, n_samples = 259/862, baseline = 0.5406, acc = 0.6882
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 99, n_samples = 264/862, baseline = 0.5385, acc = 0.6756
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 0, n_samples = 248/862, baseline = 0.5505, acc = 0.6857
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 1, n_samples = 240/862, baseline = 0.5740, acc = 0.6704
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 2, n_samples = 283/862, baseline = 0.5337, acc = 0.6684
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 3, n_samples = 270/862, baseline = 0.5490, acc = 0.6926
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 4, n_samples = 227/862, baseline = 0.5543, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 5, n_samples = 241/862, baseline = 0.5572, acc = 0.6683
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 6, n_samples = 253/862, baseline = 0.5764, acc = 0.6864
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 7, n_samples = 255/862, baseline = 0.5519, acc = 0.6755
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 8, n_samples = 249/862, baseline = 0.5563, acc = 0.6721
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 9, n_samples = 275/862, baseline = 0.5417, acc = 0.6371
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 10, n_samples = 239/862, baseline = 0.5297, acc = 0.6661
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 11, n_samples = 261/862, baseline = 0.5491, acc = 0.6622
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 12, n_samples = 255/862, baseline = 0.5568, acc = 0.6656
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 13, n_samples = 260/862, baseline = 0.5349, acc = 0.6860
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 14, n_samples = 258/862, baseline = 0.5546, acc = 0.6854
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 15, n_samples = 263/862, baseline = 0.5609, acc = 0.6745
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 16, n_samples = 254/862, baseline = 0.5493, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 17, n_samples = 234/862, baseline = 0.5669, acc = 0.6911
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 18, n_samples = 249/862, baseline = 0.5498, acc = 0.6770
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 19, n_samples = 264/862, baseline = 0.5702, acc = 0.6773
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 20, n_samples = 239/862, baseline = 0.5554, acc = 0.6838
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 21, n_samples = 268/862, baseline = 0.5404, acc = 0.6886
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 22, n_samples = 258/862, baseline = 0.5530, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 23, n_samples = 282/862, baseline = 0.5362, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 24, n_samples = 263/862, baseline = 0.5559, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 25, n_samples = 262/862, baseline = 0.5433, acc = 0.6617
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 26, n_samples = 289/862, baseline = 0.5689, acc = 0.6754
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 27, n_samples = 246/862, baseline = 0.5682, acc = 0.6899
-- [u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 28, n_samples = 267/862, baseline = 0.5513, acc = 0.6723
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 29, n_samples = 265/862, baseline = 0.5628, acc = 0.6399
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 30, n_samples = 264/862, baseline = 0.5569, acc = 0.6806
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 31, n_samples = 239/862, baseline = 0.5490, acc = 0.6613
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 32, n_samples = 252/862, baseline = 0.5639, acc = 0.6492
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 33, n_samples = 269/862, baseline = 0.5767, acc = 0.6762
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 34, n_samples = 269/862, baseline = 0.5599, acc = 0.6728
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 35, n_samples = 260/862, baseline = 0.5581, acc = 0.6910
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 36, n_samples = 262/862, baseline = 0.5517, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 37, n_samples = 277/862, baseline = 0.5538, acc = 0.6769
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 38, n_samples = 267/862, baseline = 0.5714, acc = 0.6891
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 39, n_samples = 237/862, baseline = 0.5504, acc = 0.6912
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 40, n_samples = 281/862, baseline = 0.5491, acc = 0.6988
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 41, n_samples = 247/862, baseline = 0.5496, acc = 0.6894
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 42, n_samples = 255/862, baseline = 0.5486, acc = 0.6804
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 43, n_samples = 227/862, baseline = 0.5370, acc = 0.6819
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 44, n_samples = 265/862, baseline = 0.5595, acc = 0.6868
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 45, n_samples = 270/862, baseline = 0.5287, acc = 0.6689
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 46, n_samples = 236/862, baseline = 0.5495, acc = 0.6853
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 47, n_samples = 260/862, baseline = 0.5515, acc = 0.6628
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 48, n_samples = 257/862, baseline = 0.5471, acc = 0.6760
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 49, n_samples = 254/862, baseline = 0.5543, acc = 0.6694
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 50, n_samples = 279/862, baseline = 0.5472, acc = 0.6810
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 51, n_samples = 264/862, baseline = 0.5452, acc = 0.6522
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 52, n_samples = 264/862, baseline = 0.5452, acc = 0.6722
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 53, n_samples = 277/862, baseline = 0.5641, acc = 0.6821
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 54, n_samples = 248/862, baseline = 0.5472, acc = 0.6792
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 55, n_samples = 275/862, baseline = 0.5486, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 56, n_samples = 290/862, baseline = 0.5315, acc = 0.6696
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 57, n_samples = 278/862, baseline = 0.5719, acc = 0.6747
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 58, n_samples = 260/862, baseline = 0.5449, acc = 0.6910
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 59, n_samples = 266/862, baseline = 0.5386, acc = 0.6829
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 60, n_samples = 260/862, baseline = 0.5465, acc = 0.6811
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 61, n_samples = 259/862, baseline = 0.5572, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 62, n_samples = 262/862, baseline = 0.5667, acc = 0.6733
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 63, n_samples = 276/862, baseline = 0.5648, acc = 0.6741
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 64, n_samples = 281/862, baseline = 0.5628, acc = 0.6764
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 65, n_samples = 266/862, baseline = 0.5352, acc = 0.6762
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 66, n_samples = 261/862, baseline = 0.5541, acc = 0.6872
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 67, n_samples = 258/862, baseline = 0.5579, acc = 0.6854
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 68, n_samples = 259/862, baseline = 0.5605, acc = 0.6451
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 69, n_samples = 274/862, baseline = 0.5476, acc = 0.6769
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 70, n_samples = 263/862, baseline = 0.5543, acc = 0.6761
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 71, n_samples = 254/862, baseline = 0.5378, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 72, n_samples = 244/862, baseline = 0.5518, acc = 0.6472
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 73, n_samples = 275/862, baseline = 0.5690, acc = 0.6848
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 74, n_samples = 243/862, baseline = 0.5477, acc = 0.6785
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 75, n_samples = 259/862, baseline = 0.5622, acc = 0.6799
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 76, n_samples = 259/862, baseline = 0.5473, acc = 0.6965
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 77, n_samples = 267/862, baseline = 0.5496, acc = 0.6807
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 78, n_samples = 232/862, baseline = 0.5381, acc = 0.6683
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 79, n_samples = 256/862, baseline = 0.5479, acc = 0.6485
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 80, n_samples = 260/862, baseline = 0.5598, acc = 0.6910
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 81, n_samples = 268/862, baseline = 0.5640, acc = 0.6717
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 82, n_samples = 277/862, baseline = 0.5231, acc = 0.6701
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 83, n_samples = 246/862, baseline = 0.5666, acc = 0.6769
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 84, n_samples = 259/862, baseline = 0.5688, acc = 0.6617
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 85, n_samples = 250/862, baseline = 0.5408, acc = 0.6748
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 86, n_samples = 240/862, baseline = 0.5547, acc = 0.6881
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 87, n_samples = 240/862, baseline = 0.5354, acc = 0.6704
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 88, n_samples = 256/862, baseline = 0.5594, acc = 0.6914
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 89, n_samples = 260/862, baseline = 0.5415, acc = 0.6777
-- [u'Max D', 'Volume', 'Max_Dose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 90, n_samples = 244/862, baseline = 0.5663, acc = 0.6731
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 91, n_samples = 248/862, baseline = 0.5456, acc = 0.6743
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 92, n_samples = 242/862, baseline = 0.5565, acc = 0.6613
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 93, n_samples = 240/862, baseline = 0.5434, acc = 0.6704
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 94, n_samples = 266/862, baseline = 0.5453, acc = 0.6862
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 95, n_samples = 259/862, baseline = 0.5456, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 96, n_samples = 288/862, baseline = 0.5418, acc = 0.6585
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 97, n_samples = 249/862, baseline = 0.5775, acc = 0.6835
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 98, n_samples = 245/862, baseline = 0.5478, acc = 0.6791
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 99, n_samples = 256/862, baseline = 0.5363, acc = 0.6617
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 0, n_samples = 254/862, baseline = 0.5592, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 1, n_samples = 246/862, baseline = 0.5617, acc = 0.6640
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 2, n_samples = 263/862, baseline = 0.5526, acc = 0.6644
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 3, n_samples = 261/862, baseline = 0.5491, acc = 0.6905
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 4, n_samples = 270/862, baseline = 0.5473, acc = 0.6993
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 5, n_samples = 290/862, baseline = 0.5559, acc = 0.6661
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 6, n_samples = 238/862, baseline = 0.5465, acc = 0.6955
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 7, n_samples = 261/862, baseline = 0.5524, acc = 0.6855
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 8, n_samples = 247/862, baseline = 0.5561, acc = 0.7073
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 9, n_samples = 254/862, baseline = 0.5576, acc = 0.6678
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 10, n_samples = 270/862, baseline = 0.5608, acc = 0.6976
-- ['Age', u'SM', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 11, n_samples = 263/862, baseline = 0.5576, acc = 0.7045
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 12, n_samples = 291/862, baseline = 0.5377, acc = 0.6760
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 13, n_samples = 263/862, baseline = 0.5359, acc = 0.6678
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 14, n_samples = 258/862, baseline = 0.5546, acc = 0.6805
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 15, n_samples = 247/862, baseline = 0.5561, acc = 0.6927
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 16, n_samples = 272/862, baseline = 0.5492, acc = 0.7000
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 17, n_samples = 277/862, baseline = 0.5504, acc = 0.7197
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 18, n_samples = 251/862, baseline = 0.5516, acc = 0.6972
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 19, n_samples = 230/862, baseline = 0.5459, acc = 0.6835
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 20, n_samples = 250/862, baseline = 0.5654, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 21, n_samples = 252/862, baseline = 0.5525, acc = 0.6852
-- ['Age', u'SM', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 22, n_samples = 267/862, baseline = 0.5630, acc = 0.6941
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 23, n_samples = 251/862, baseline = 0.5466, acc = 0.6809
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 24, n_samples = 263/862, baseline = 0.5543, acc = 0.6795
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 25, n_samples = 266/862, baseline = 0.5554, acc = 0.6913
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 26, n_samples = 244/862, baseline = 0.5502, acc = 0.6958
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 27, n_samples = 262/862, baseline = 0.5533, acc = 0.6767
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 28, n_samples = 250/862, baseline = 0.5425, acc = 0.6895
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 29, n_samples = 267/862, baseline = 0.5496, acc = 0.6824
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 30, n_samples = 257/862, baseline = 0.5455, acc = 0.6942
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 31, n_samples = 262/862, baseline = 0.5550, acc = 0.6917
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 32, n_samples = 248/862, baseline = 0.5586, acc = 0.6792
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 33, n_samples = 264/862, baseline = 0.5485, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 34, n_samples = 270/862, baseline = 0.5642, acc = 0.6926
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 35, n_samples = 256/862, baseline = 0.5512, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 36, n_samples = 270/862, baseline = 0.5608, acc = 0.6875
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 37, n_samples = 271/862, baseline = 0.5415, acc = 0.6819
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 38, n_samples = 235/862, baseline = 0.5598, acc = 0.6986
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 39, n_samples = 247/862, baseline = 0.5675, acc = 0.6829
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 40, n_samples = 248/862, baseline = 0.5733, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 41, n_samples = 260/862, baseline = 0.5631, acc = 0.6944
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 42, n_samples = 243/862, baseline = 0.5493, acc = 0.6672
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 43, n_samples = 259/862, baseline = 0.5638, acc = 0.6667
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 44, n_samples = 235/862, baseline = 0.5518, acc = 0.6826
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 45, n_samples = 260/862, baseline = 0.5482, acc = 0.6894
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 46, n_samples = 256/862, baseline = 0.5611, acc = 0.6782
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 47, n_samples = 263/862, baseline = 0.5392, acc = 0.6678
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 48, n_samples = 254/862, baseline = 0.5576, acc = 0.7072
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 49, n_samples = 271/862, baseline = 0.5499, acc = 0.6853
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 50, n_samples = 259/862, baseline = 0.5638, acc = 0.6866
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 51, n_samples = 237/862, baseline = 0.5632, acc = 0.6768
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 52, n_samples = 258/862, baseline = 0.5430, acc = 0.6921
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 53, n_samples = 272/862, baseline = 0.5492, acc = 0.6610
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 54, n_samples = 267/862, baseline = 0.5630, acc = 0.6941
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 55, n_samples = 255/862, baseline = 0.5404, acc = 0.6787
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 56, n_samples = 248/862, baseline = 0.5537, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 57, n_samples = 252/862, baseline = 0.5689, acc = 0.6984
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 58, n_samples = 276/862, baseline = 0.5580, acc = 0.7048
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 59, n_samples = 254/862, baseline = 0.5444, acc = 0.6760
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 60, n_samples = 261/862, baseline = 0.5624, acc = 0.6772
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 61, n_samples = 284/862, baseline = 0.5484, acc = 0.6834
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 62, n_samples = 241/862, baseline = 0.5604, acc = 0.7101
-- [u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 63, n_samples = 256/862, baseline = 0.5495, acc = 0.6848
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 64, n_samples = 272/862, baseline = 0.5678, acc = 0.6729
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 65, n_samples = 262/862, baseline = 0.5333, acc = 0.6733
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 66, n_samples = 274/862, baseline = 0.5408, acc = 0.6837
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 67, n_samples = 265/862, baseline = 0.5494, acc = 0.6884
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 68, n_samples = 261/862, baseline = 0.5607, acc = 0.6905
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 69, n_samples = 238/862, baseline = 0.5465, acc = 0.6827
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 70, n_samples = 269/862, baseline = 0.5531, acc = 0.6610
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 71, n_samples = 289/862, baseline = 0.5410, acc = 0.6911
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 72, n_samples = 267/862, baseline = 0.5597, acc = 0.6992
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 73, n_samples = 249/862, baseline = 0.5400, acc = 0.6998
-- ['Age', u'Max D', 'Volume', u'Embo', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 74, n_samples = 283/862, baseline = 0.5475, acc = 0.6857
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 75, n_samples = 258/862, baseline = 0.5546, acc = 0.7169
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 76, n_samples = 272/862, baseline = 0.5271, acc = 0.6881
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 77, n_samples = 252/862, baseline = 0.5262, acc = 0.6656
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 78, n_samples = 260/862, baseline = 0.5465, acc = 0.7010
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 79, n_samples = 258/862, baseline = 0.5629, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 80, n_samples = 220/862, baseline = 0.5312, acc = 0.6729
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 81, n_samples = 247/862, baseline = 0.5447, acc = 0.6927
-- ['Age', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 82, n_samples = 241/862, baseline = 0.5411, acc = 0.6457
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 83, n_samples = 264/862, baseline = 0.5452, acc = 0.6689
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 84, n_samples = 275/862, baseline = 0.5520, acc = 0.6712
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 85, n_samples = 249/862, baseline = 0.5481, acc = 0.6754
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 86, n_samples = 265/862, baseline = 0.5511, acc = 0.6834
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 87, n_samples = 251/862, baseline = 0.5450, acc = 0.6678
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 88, n_samples = 257/862, baseline = 0.5455, acc = 0.6529
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 89, n_samples = 245/862, baseline = 0.5624, acc = 0.6921
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 90, n_samples = 249/862, baseline = 0.5481, acc = 0.6639
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 91, n_samples = 256/862, baseline = 0.5594, acc = 0.6997
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 92, n_samples = 287/862, baseline = 0.5461, acc = 0.6539
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 93, n_samples = 249/862, baseline = 0.5302, acc = 0.6721
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 94, n_samples = 260/862, baseline = 0.5731, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 95, n_samples = 265/862, baseline = 0.5377, acc = 0.6767
-- ['Age', u'SM', u'Max D', 'Volume', u'Embo', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 96, n_samples = 250/862, baseline = 0.5735, acc = 0.6307
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 97, n_samples = 253/862, baseline = 0.5681, acc = 0.6962
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 98, n_samples = 265/862, baseline = 0.5528, acc = 0.6549
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 99, n_samples = 240/862, baseline = 0.5579, acc = 0.6913
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 0, n_samples = 174/862, baseline = 0.5596, acc = 0.6715
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 1, n_samples = 158/862, baseline = 0.5497, acc = 0.6733
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 2, n_samples = 181/862, baseline = 0.5609, acc = 0.6858
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 3, n_samples = 182/862, baseline = 0.5588, acc = 0.6765
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 4, n_samples = 166/862, baseline = 0.5560, acc = 0.6739
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 5, n_samples = 169/862, baseline = 0.5469, acc = 0.6364
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 6, n_samples = 163/862, baseline = 0.5536, acc = 0.6681
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 7, n_samples = 172/862, baseline = 0.5580, acc = 0.6725
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 8, n_samples = 146/862, baseline = 0.5489, acc = 0.6816
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 9, n_samples = 162/862, baseline = 0.5400, acc = 0.6743
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 10, n_samples = 168/862, baseline = 0.5533, acc = 0.6643
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 11, n_samples = 160/862, baseline = 0.5413, acc = 0.6638
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 12, n_samples = 192/862, baseline = 0.5597, acc = 0.6925
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 13, n_samples = 189/862, baseline = 0.5587, acc = 0.6523
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 14, n_samples = 160/862, baseline = 0.5513, acc = 0.6766
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 15, n_samples = 158/862, baseline = 0.5511, acc = 0.6776
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 16, n_samples = 169/862, baseline = 0.5570, acc = 0.6869
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 17, n_samples = 166/862, baseline = 0.5417, acc = 0.6724
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 18, n_samples = 165/862, baseline = 0.5423, acc = 0.6700
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 19, n_samples = 169/862, baseline = 0.5584, acc = 0.6595
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 20, n_samples = 172/862, baseline = 0.5536, acc = 0.6710
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 21, n_samples = 162/862, baseline = 0.5514, acc = 0.6671
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 22, n_samples = 153/862, baseline = 0.5458, acc = 0.6827
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 23, n_samples = 185/862, baseline = 0.5436, acc = 0.6588
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 24, n_samples = 180/862, baseline = 0.5674, acc = 0.6789
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 25, n_samples = 165/862, baseline = 0.5552, acc = 0.6772
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 26, n_samples = 172/862, baseline = 0.5377, acc = 0.6797
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 27, n_samples = 168/862, baseline = 0.5418, acc = 0.6671
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 28, n_samples = 166/862, baseline = 0.5503, acc = 0.6710
-- [u'Max D', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 29, n_samples = 158/862, baseline = 0.5469, acc = 0.6605
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 30, n_samples = 175/862, baseline = 0.5328, acc = 0.6361
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 31, n_samples = 199/862, baseline = 0.5339, acc = 0.6576
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 32, n_samples = 172/862, baseline = 0.5609, acc = 0.6725
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 33, n_samples = 156/862, baseline = 0.5581, acc = 0.6785
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 34, n_samples = 162/862, baseline = 0.5614, acc = 0.6286
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 35, n_samples = 175/862, baseline = 0.5531, acc = 0.6914
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 36, n_samples = 184/862, baseline = 0.5516, acc = 0.6652
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 37, n_samples = 175/862, baseline = 0.5531, acc = 0.6696
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 38, n_samples = 167/862, baseline = 0.5396, acc = 0.6734
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 39, n_samples = 181/862, baseline = 0.5580, acc = 0.6725
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 40, n_samples = 161/862, baseline = 0.5492, acc = 0.6847
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 41, n_samples = 174/862, baseline = 0.5581, acc = 0.6642
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 42, n_samples = 163/862, baseline = 0.5365, acc = 0.6495
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 43, n_samples = 159/862, baseline = 0.5633, acc = 0.6814
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 44, n_samples = 171/862, baseline = 0.5630, acc = 0.6599
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 45, n_samples = 168/862, baseline = 0.5476, acc = 0.6787
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 46, n_samples = 180/862, baseline = 0.5557, acc = 0.6789
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 47, n_samples = 193/862, baseline = 0.5456, acc = 0.6816
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 48, n_samples = 146/862, baseline = 0.5587, acc = 0.6620
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 49, n_samples = 193/862, baseline = 0.5411, acc = 0.6592
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 50, n_samples = 170/862, baseline = 0.5636, acc = 0.6965
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 51, n_samples = 177/862, baseline = 0.5533, acc = 0.6672
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 52, n_samples = 153/862, baseline = 0.5684, acc = 0.6446
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 53, n_samples = 165/862, baseline = 0.5481, acc = 0.6714
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 54, n_samples = 180/862, baseline = 0.5528, acc = 0.6833
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 55, n_samples = 176/862, baseline = 0.5598, acc = 0.6647
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 56, n_samples = 171/862, baseline = 0.5514, acc = 0.6729
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 57, n_samples = 177/862, baseline = 0.5533, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 58, n_samples = 160/862, baseline = 0.5413, acc = 0.6781
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 59, n_samples = 181/862, baseline = 0.5419, acc = 0.6696
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 60, n_samples = 156/862, baseline = 0.5666, acc = 0.6516
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 61, n_samples = 159/862, baseline = 0.5505, acc = 0.6757
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 62, n_samples = 175/862, baseline = 0.5313, acc = 0.6434
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 63, n_samples = 166/862, baseline = 0.5546, acc = 0.6753
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 64, n_samples = 157/862, baseline = 0.5560, acc = 0.6723
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 65, n_samples = 177/862, baseline = 0.5474, acc = 0.6628
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 66, n_samples = 153/862, baseline = 0.5614, acc = 0.6869
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 67, n_samples = 168/862, baseline = 0.5432, acc = 0.6758
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 68, n_samples = 149/862, baseline = 0.5498, acc = 0.6844
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 69, n_samples = 192/862, baseline = 0.5448, acc = 0.6836
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 70, n_samples = 202/862, baseline = 0.5424, acc = 0.6697
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 71, n_samples = 166/862, baseline = 0.5489, acc = 0.6911
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 72, n_samples = 207/862, baseline = 0.5435, acc = 0.6840
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 73, n_samples = 161/862, baseline = 0.5407, acc = 0.6690
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 74, n_samples = 162/862, baseline = 0.5371, acc = 0.6743
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 75, n_samples = 175/862, baseline = 0.5473, acc = 0.6667
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 76, n_samples = 183/862, baseline = 0.5493, acc = 0.6775
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 77, n_samples = 174/862, baseline = 0.5538, acc = 0.6672
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 78, n_samples = 156/862, baseline = 0.5524, acc = 0.6827
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 79, n_samples = 187/862, baseline = 0.5467, acc = 0.6681
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 80, n_samples = 165/862, baseline = 0.5538, acc = 0.6872
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 81, n_samples = 178/862, baseline = 0.5468, acc = 0.6798
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 82, n_samples = 197/862, baseline = 0.5534, acc = 0.6842
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 83, n_samples = 160/862, baseline = 0.5541, acc = 0.6809
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 84, n_samples = 180/862, baseline = 0.5557, acc = 0.6496
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 85, n_samples = 170/862, baseline = 0.5665, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 86, n_samples = 170/862, baseline = 0.5506, acc = 0.6893
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 87, n_samples = 154/862, baseline = 0.5410, acc = 0.6681
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 88, n_samples = 189/862, baseline = 0.5557, acc = 0.6493
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 89, n_samples = 160/862, baseline = 0.5513, acc = 0.6766
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 90, n_samples = 150/862, baseline = 0.5463, acc = 0.6728
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 91, n_samples = 190/862, baseline = 0.5536, acc = 0.6845
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 92, n_samples = 156/862, baseline = 0.5567, acc = 0.6785
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 93, n_samples = 169/862, baseline = 0.5512, acc = 0.6811
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 94, n_samples = 168/862, baseline = 0.5490, acc = 0.6816
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 95, n_samples = 170/862, baseline = 0.5665, acc = 0.6951
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 96, n_samples = 181/862, baseline = 0.5448, acc = 0.6637
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 97, n_samples = 170/862, baseline = 0.5491, acc = 0.6647
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=False iter = 98, n_samples = 149/862, baseline = 0.5526, acc = 0.6746
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=False iter = 99, n_samples = 197/862, baseline = 0.5474, acc = 0.6782
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 0, n_samples = 205/862, baseline = 0.5540, acc = 0.6865
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 1, n_samples = 158/862, baseline = 0.5440, acc = 0.6662
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 2, n_samples = 164/862, baseline = 0.5673, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 3, n_samples = 161/862, baseline = 0.5478, acc = 0.6862
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 4, n_samples = 158/862, baseline = 0.5568, acc = 0.6591
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 5, n_samples = 179/862, baseline = 0.5593, acc = 0.6823
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 6, n_samples = 151/862, baseline = 0.5401, acc = 0.6554
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 7, n_samples = 172/862, baseline = 0.5594, acc = 0.6928
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 8, n_samples = 169/862, baseline = 0.5498, acc = 0.6955
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 9, n_samples = 168/862, baseline = 0.5375, acc = 0.6729
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 10, n_samples = 165/862, baseline = 0.5538, acc = 0.6887
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 11, n_samples = 171/862, baseline = 0.5716, acc = 0.6556
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 12, n_samples = 179/862, baseline = 0.5608, acc = 0.6633
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 13, n_samples = 183/862, baseline = 0.5434, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 14, n_samples = 191/862, baseline = 0.5410, acc = 0.6766
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 15, n_samples = 176/862, baseline = 0.5437, acc = 0.6706
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 16, n_samples = 177/862, baseline = 0.5693, acc = 0.6569
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 17, n_samples = 186/862, baseline = 0.5473, acc = 0.6967
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 18, n_samples = 183/862, baseline = 0.5434, acc = 0.6730
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 19, n_samples = 154/862, baseline = 0.5579, acc = 0.6695
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 20, n_samples = 178/862, baseline = 0.5468, acc = 0.6769
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 21, n_samples = 171/862, baseline = 0.5499, acc = 0.6758
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 22, n_samples = 181/862, baseline = 0.5580, acc = 0.6902
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 23, n_samples = 168/862, baseline = 0.5490, acc = 0.6729
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 24, n_samples = 158/862, baseline = 0.5554, acc = 0.6719
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 25, n_samples = 175/862, baseline = 0.5619, acc = 0.6958
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 26, n_samples = 185/862, baseline = 0.5510, acc = 0.6647
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 27, n_samples = 148/862, baseline = 0.5574, acc = 0.6821
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 28, n_samples = 163/862, baseline = 0.5494, acc = 0.6853
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 29, n_samples = 183/862, baseline = 0.5538, acc = 0.6701
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 30, n_samples = 167/862, baseline = 0.5468, acc = 0.6619
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 31, n_samples = 157/862, baseline = 0.5390, acc = 0.6709
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 32, n_samples = 169/862, baseline = 0.5570, acc = 0.6840
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 33, n_samples = 174/862, baseline = 0.5494, acc = 0.6831
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 34, n_samples = 186/862, baseline = 0.5533, acc = 0.6538
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 35, n_samples = 174/862, baseline = 0.5509, acc = 0.6759
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 36, n_samples = 169/862, baseline = 0.5455, acc = 0.6782
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 37, n_samples = 184/862, baseline = 0.5560, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=False iter = 38, n_samples = 168/862, baseline = 0.5605, acc = 0.6657
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 39, n_samples = 155/862, baseline = 0.5573, acc = 0.6393
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 40, n_samples = 164/862, baseline = 0.5602, acc = 0.6619
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 41, n_samples = 160/862, baseline = 0.5456, acc = 0.6781
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 42, n_samples = 171/862, baseline = 0.5557, acc = 0.6946
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 43, n_samples = 169/862, baseline = 0.5570, acc = 0.6696
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 44, n_samples = 179/862, baseline = 0.5403, acc = 0.6779
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 45, n_samples = 174/862, baseline = 0.5334, acc = 0.6599
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 46, n_samples = 164/862, baseline = 0.5487, acc = 0.6777
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 47, n_samples = 178/862, baseline = 0.5512, acc = 0.6959
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 48, n_samples = 184/862, baseline = 0.5531, acc = 0.6829
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 49, n_samples = 169/862, baseline = 0.5483, acc = 0.6825
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 50, n_samples = 174/862, baseline = 0.5523, acc = 0.6715
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 51, n_samples = 163/862, baseline = 0.5451, acc = 0.6881
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 52, n_samples = 166/862, baseline = 0.5632, acc = 0.6897
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 53, n_samples = 167/862, baseline = 0.5410, acc = 0.6906
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 54, n_samples = 169/862, baseline = 0.5584, acc = 0.6854
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 55, n_samples = 165/862, baseline = 0.5352, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 56, n_samples = 176/862, baseline = 0.5569, acc = 0.7012
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 57, n_samples = 177/862, baseline = 0.5314, acc = 0.6526
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 58, n_samples = 170/862, baseline = 0.5405, acc = 0.6662
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 59, n_samples = 164/862, baseline = 0.5516, acc = 0.6762
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 60, n_samples = 174/862, baseline = 0.5378, acc = 0.6715
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 61, n_samples = 152/862, baseline = 0.5535, acc = 0.6732
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 62, n_samples = 177/862, baseline = 0.5518, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 63, n_samples = 182/862, baseline = 0.5441, acc = 0.6794
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 64, n_samples = 170/862, baseline = 0.5535, acc = 0.6908
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 65, n_samples = 176/862, baseline = 0.5554, acc = 0.6735
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 66, n_samples = 155/862, baseline = 0.5545, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 67, n_samples = 180/862, baseline = 0.5411, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 68, n_samples = 182/862, baseline = 0.5500, acc = 0.6750
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 69, n_samples = 172/862, baseline = 0.5464, acc = 0.6783
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 70, n_samples = 169/862, baseline = 0.5541, acc = 0.6811
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 71, n_samples = 169/862, baseline = 0.5613, acc = 0.6421
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 72, n_samples = 184/862, baseline = 0.5708, acc = 0.6519
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 73, n_samples = 171/862, baseline = 0.5601, acc = 0.6802
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 74, n_samples = 190/862, baseline = 0.5610, acc = 0.6994
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 75, n_samples = 173/862, baseline = 0.5399, acc = 0.6676
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 76, n_samples = 183/862, baseline = 0.5523, acc = 0.6951
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 77, n_samples = 153/862, baseline = 0.5501, acc = 0.6685
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 78, n_samples = 177/862, baseline = 0.5504, acc = 0.6774
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 79, n_samples = 157/862, baseline = 0.5489, acc = 0.6723
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 80, n_samples = 167/862, baseline = 0.5511, acc = 0.6719
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 81, n_samples = 174/862, baseline = 0.5392, acc = 0.6744
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 82, n_samples = 189/862, baseline = 0.5423, acc = 0.6776
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 83, n_samples = 189/862, baseline = 0.5319, acc = 0.6627
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 84, n_samples = 174/862, baseline = 0.5610, acc = 0.6701
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 85, n_samples = 192/862, baseline = 0.5388, acc = 0.6881
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 86, n_samples = 177/862, baseline = 0.5620, acc = 0.6774
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 87, n_samples = 175/862, baseline = 0.5488, acc = 0.6681
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 88, n_samples = 178/862, baseline = 0.5570, acc = 0.6769
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 89, n_samples = 169/862, baseline = 0.5440, acc = 0.6768
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 90, n_samples = 170/862, baseline = 0.5520, acc = 0.6676
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 91, n_samples = 178/862, baseline = 0.5482, acc = 0.6637
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 92, n_samples = 153/862, baseline = 0.5543, acc = 0.6925
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 93, n_samples = 172/862, baseline = 0.5507, acc = 0.6855
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 94, n_samples = 173/862, baseline = 0.5646, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 95, n_samples = 168/862, baseline = 0.5504, acc = 0.6888
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 96, n_samples = 158/862, baseline = 0.5597, acc = 0.6619
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 97, n_samples = 168/862, baseline = 0.5634, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=False iter = 98, n_samples = 170/862, baseline = 0.5462, acc = 0.6835
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=False iter = 99, n_samples = 188/862, baseline = 0.5579, acc = 0.6766
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 0, n_samples = 159/862, baseline = 0.5533, acc = 0.6828
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 1, n_samples = 173/862, baseline = 0.5501, acc = 0.6967
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 2, n_samples = 143/862, baseline = 0.5480, acc = 0.6732
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 3, n_samples = 179/862, baseline = 0.5359, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 4, n_samples = 150/862, baseline = 0.5449, acc = 0.6728
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 5, n_samples = 185/862, baseline = 0.5451, acc = 0.6942
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 6, n_samples = 162/862, baseline = 0.5486, acc = 0.6657
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 7, n_samples = 177/862, baseline = 0.5679, acc = 0.6642
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 8, n_samples = 172/862, baseline = 0.5609, acc = 0.6899
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 9, n_samples = 164/862, baseline = 0.5501, acc = 0.6777
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 10, n_samples = 163/862, baseline = 0.5565, acc = 0.6753
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 11, n_samples = 165/862, baseline = 0.5710, acc = 0.6815
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 12, n_samples = 173/862, baseline = 0.5515, acc = 0.6865
-- ['Age', u'Max D', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 13, n_samples = 187/862, baseline = 0.5437, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 14, n_samples = 177/862, baseline = 0.5387, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 15, n_samples = 172/862, baseline = 0.5565, acc = 0.6841
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Aneurysm', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 16, n_samples = 169/862, baseline = 0.5556, acc = 0.7027
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 17, n_samples = 181/862, baseline = 0.5433, acc = 0.6769
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 18, n_samples = 170/862, baseline = 0.5477, acc = 0.6821
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 19, n_samples = 172/862, baseline = 0.5435, acc = 0.6725
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 20, n_samples = 166/862, baseline = 0.5417, acc = 0.6897
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 21, n_samples = 172/862, baseline = 0.5449, acc = 0.6913
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 22, n_samples = 168/862, baseline = 0.5533, acc = 0.7017
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 23, n_samples = 175/862, baseline = 0.5444, acc = 0.6667
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 24, n_samples = 178/862, baseline = 0.5556, acc = 0.6652
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 25, n_samples = 186/862, baseline = 0.5577, acc = 0.6790
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 26, n_samples = 179/862, baseline = 0.5549, acc = 0.6559
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 27, n_samples = 193/862, baseline = 0.5381, acc = 0.6682
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 28, n_samples = 160/862, baseline = 0.5470, acc = 0.6695
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 29, n_samples = 180/862, baseline = 0.5425, acc = 0.6804
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 30, n_samples = 148/862, baseline = 0.5420, acc = 0.6639
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 31, n_samples = 165/862, baseline = 0.5452, acc = 0.6872
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 32, n_samples = 180/862, baseline = 0.5513, acc = 0.6950
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 33, n_samples = 175/862, baseline = 0.5691, acc = 0.6885
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 34, n_samples = 184/862, baseline = 0.5693, acc = 0.6755
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 35, n_samples = 159/862, baseline = 0.5448, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 36, n_samples = 169/862, baseline = 0.5584, acc = 0.6768
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 37, n_samples = 170/862, baseline = 0.5491, acc = 0.6893
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 38, n_samples = 165/862, baseline = 0.5452, acc = 0.6743
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 39, n_samples = 174/862, baseline = 0.5465, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 40, n_samples = 165/862, baseline = 0.5567, acc = 0.6628
-- [u'Max D', 'Volume', 'Max_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 41, n_samples = 170/862, baseline = 0.5434, acc = 0.6777
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 42, n_samples = 181/862, baseline = 0.5521, acc = 0.6579
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 43, n_samples = 167/862, baseline = 0.5353, acc = 0.6619
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 44, n_samples = 175/862, baseline = 0.5517, acc = 0.6783
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 45, n_samples = 158/862, baseline = 0.5483, acc = 0.6676
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 46, n_samples = 191/862, baseline = 0.5604, acc = 0.6930
-- ['Age', u'Max D', 'Volume', u'Draining_Vein_Depth', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 47, n_samples = 175/862, baseline = 0.5662, acc = 0.6856
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 48, n_samples = 191/862, baseline = 0.5499, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 49, n_samples = 173/862, baseline = 0.5443, acc = 0.6792
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 50, n_samples = 169/862, baseline = 0.5498, acc = 0.6912
-- ['Age', u'Max D', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 51, n_samples = 182/862, baseline = 0.5368, acc = 0.6794
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 52, n_samples = 188/862, baseline = 0.5608, acc = 0.6677
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 53, n_samples = 194/862, baseline = 0.5449, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 54, n_samples = 165/862, baseline = 0.5438, acc = 0.6844
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 55, n_samples = 148/862, baseline = 0.5462, acc = 0.6681
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 56, n_samples = 172/862, baseline = 0.5435, acc = 0.6899
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 57, n_samples = 177/862, baseline = 0.5533, acc = 0.6803
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 58, n_samples = 163/862, baseline = 0.5622, acc = 0.6624
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 59, n_samples = 166/862, baseline = 0.5517, acc = 0.6882
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 60, n_samples = 182/862, baseline = 0.5529, acc = 0.6912
-- ['Age', u'Max D', 'Volume', u'Embo', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 61, n_samples = 177/862, baseline = 0.5504, acc = 0.6964
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 62, n_samples = 182/862, baseline = 0.5544, acc = 0.6794
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 63, n_samples = 148/862, baseline = 0.5560, acc = 0.6709
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 64, n_samples = 149/862, baseline = 0.5568, acc = 0.6690
-- ['Age', u'SM', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 65, n_samples = 156/862, baseline = 0.5510, acc = 0.6856
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 66, n_samples = 197/862, baseline = 0.5489, acc = 0.6887
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 67, n_samples = 170/862, baseline = 0.5694, acc = 0.6590
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 68, n_samples = 153/862, baseline = 0.5472, acc = 0.6756
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 69, n_samples = 157/862, baseline = 0.5617, acc = 0.6879
-- ['Age', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 70, n_samples = 180/862, baseline = 0.5499, acc = 0.6701
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 71, n_samples = 174/862, baseline = 0.5567, acc = 0.6701
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 72, n_samples = 187/862, baseline = 0.5570, acc = 0.6815
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 73, n_samples = 164/862, baseline = 0.5587, acc = 0.6719
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 74, n_samples = 177/862, baseline = 0.5460, acc = 0.6876
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 75, n_samples = 168/862, baseline = 0.5447, acc = 0.6744
-- ['Age', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 76, n_samples = 160/862, baseline = 0.5413, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 77, n_samples = 193/862, baseline = 0.5531, acc = 0.6891
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 78, n_samples = 191/862, baseline = 0.5469, acc = 0.6721
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 79, n_samples = 163/862, baseline = 0.5594, acc = 0.6867
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 80, n_samples = 181/862, baseline = 0.5360, acc = 0.6608
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 81, n_samples = 162/862, baseline = 0.5386, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 82, n_samples = 152/862, baseline = 0.5408, acc = 0.6732
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 83, n_samples = 176/862, baseline = 0.5583, acc = 0.6735
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 84, n_samples = 171/862, baseline = 0.5412, acc = 0.6700
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 85, n_samples = 176/862, baseline = 0.5335, acc = 0.6720
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 86, n_samples = 182/862, baseline = 0.5368, acc = 0.6882
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 87, n_samples = 178/862, baseline = 0.5614, acc = 0.6944
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 88, n_samples = 177/862, baseline = 0.5693, acc = 0.6818
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 89, n_samples = 165/862, baseline = 0.5452, acc = 0.6614
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=False iter = 90, n_samples = 171/862, baseline = 0.5557, acc = 0.6802
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 91, n_samples = 141/862, baseline = 0.5479, acc = 0.6796
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 92, n_samples = 202/862, baseline = 0.5485, acc = 0.6727
-- [u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 93, n_samples = 170/862, baseline = 0.5607, acc = 0.6850
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 94, n_samples = 161/862, baseline = 0.5563, acc = 0.6876
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 95, n_samples = 170/862, baseline = 0.5549, acc = 0.6676
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=False iter = 96, n_samples = 176/862, baseline = 0.5525, acc = 0.6735
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 97, n_samples = 175/862, baseline = 0.5590, acc = 0.6812
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 98, n_samples = 159/862, baseline = 0.5533, acc = 0.6686
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=False iter = 99, n_samples = 152/862, baseline = 0.5606, acc = 0.6775
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 0, n_samples = 188/862, baseline = 0.5579, acc = 0.6884
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 1, n_samples = 182/862, baseline = 0.5662, acc = 0.6471
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 2, n_samples = 179/862, baseline = 0.5505, acc = 0.6823
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 3, n_samples = 183/862, baseline = 0.5420, acc = 0.6524
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 4, n_samples = 154/862, baseline = 0.5579, acc = 0.6780
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 5, n_samples = 182/862, baseline = 0.5588, acc = 0.6603
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 6, n_samples = 161/862, baseline = 0.5663, acc = 0.6790
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 7, n_samples = 168/862, baseline = 0.5490, acc = 0.6729
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 8, n_samples = 163/862, baseline = 0.5651, acc = 0.6595
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 9, n_samples = 172/862, baseline = 0.5565, acc = 0.6768
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 10, n_samples = 163/862, baseline = 0.5508, acc = 0.6738
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 11, n_samples = 179/862, baseline = 0.5505, acc = 0.6706
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 12, n_samples = 162/862, baseline = 0.5686, acc = 0.6486
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 13, n_samples = 156/862, baseline = 0.5467, acc = 0.6827
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 14, n_samples = 178/862, baseline = 0.5570, acc = 0.6827
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 15, n_samples = 181/862, baseline = 0.5389, acc = 0.6608
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 16, n_samples = 162/862, baseline = 0.5514, acc = 0.6743
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 17, n_samples = 152/862, baseline = 0.5521, acc = 0.6479
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 18, n_samples = 173/862, baseline = 0.5530, acc = 0.6734
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 19, n_samples = 184/862, baseline = 0.5413, acc = 0.6652
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 20, n_samples = 180/862, baseline = 0.5513, acc = 0.6877
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 21, n_samples = 187/862, baseline = 0.5600, acc = 0.6652
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 22, n_samples = 160/862, baseline = 0.5513, acc = 0.6610
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 23, n_samples = 157/862, baseline = 0.5475, acc = 0.6752
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 24, n_samples = 177/862, baseline = 0.5577, acc = 0.6730
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 25, n_samples = 199/862, baseline = 0.5656, acc = 0.6848
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 26, n_samples = 178/862, baseline = 0.5365, acc = 0.6696
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 27, n_samples = 156/862, baseline = 0.5482, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 28, n_samples = 157/862, baseline = 0.5489, acc = 0.6638
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 29, n_samples = 168/862, baseline = 0.5432, acc = 0.6844
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 30, n_samples = 184/862, baseline = 0.5531, acc = 0.6622
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 31, n_samples = 179/862, baseline = 0.5534, acc = 0.6691
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 32, n_samples = 189/862, baseline = 0.5438, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 33, n_samples = 170/862, baseline = 0.5405, acc = 0.6749
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 34, n_samples = 177/862, baseline = 0.5606, acc = 0.6715
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 35, n_samples = 171/862, baseline = 0.5412, acc = 0.6686
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 36, n_samples = 175/862, baseline = 0.5459, acc = 0.6798
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 37, n_samples = 165/862, baseline = 0.5524, acc = 0.6786
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 38, n_samples = 180/862, baseline = 0.5455, acc = 0.6804
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 39, n_samples = 179/862, baseline = 0.5593, acc = 0.6720
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 40, n_samples = 175/862, baseline = 0.5459, acc = 0.6870
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 41, n_samples = 168/862, baseline = 0.5490, acc = 0.6628
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 42, n_samples = 161/862, baseline = 0.5549, acc = 0.6690
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 43, n_samples = 166/862, baseline = 0.5546, acc = 0.6580
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 44, n_samples = 180/862, baseline = 0.5455, acc = 0.6657
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 45, n_samples = 166/862, baseline = 0.5474, acc = 0.6810
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 46, n_samples = 170/862, baseline = 0.5636, acc = 0.6590
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 47, n_samples = 181/862, baseline = 0.5683, acc = 0.6564
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 48, n_samples = 198/862, baseline = 0.5422, acc = 0.6732
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 49, n_samples = 179/862, baseline = 0.5256, acc = 0.6413
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 50, n_samples = 179/862, baseline = 0.5432, acc = 0.6691
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 51, n_samples = 177/862, baseline = 0.5664, acc = 0.6759
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 52, n_samples = 170/862, baseline = 0.5549, acc = 0.6590
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 53, n_samples = 186/862, baseline = 0.5370, acc = 0.6612
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 54, n_samples = 177/862, baseline = 0.5547, acc = 0.6774
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 55, n_samples = 174/862, baseline = 0.5640, acc = 0.6686
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 56, n_samples = 188/862, baseline = 0.5593, acc = 0.6899
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 57, n_samples = 197/862, baseline = 0.5398, acc = 0.6677
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 58, n_samples = 178/862, baseline = 0.5453, acc = 0.6769
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 59, n_samples = 188/862, baseline = 0.5445, acc = 0.6677
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 60, n_samples = 170/862, baseline = 0.5434, acc = 0.6691
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 61, n_samples = 176/862, baseline = 0.5539, acc = 0.6778
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 62, n_samples = 186/862, baseline = 0.5518, acc = 0.6849
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 63, n_samples = 170/862, baseline = 0.5592, acc = 0.6908
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 64, n_samples = 204/862, baseline = 0.5578, acc = 0.6672
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 65, n_samples = 177/862, baseline = 0.5591, acc = 0.6672
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 66, n_samples = 173/862, baseline = 0.5327, acc = 0.6647
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 67, n_samples = 194/862, baseline = 0.5494, acc = 0.6811
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 68, n_samples = 172/862, baseline = 0.5565, acc = 0.6739
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 69, n_samples = 178/862, baseline = 0.5468, acc = 0.6696
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 70, n_samples = 149/862, baseline = 0.5596, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 71, n_samples = 162/862, baseline = 0.5543, acc = 0.6857
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 72, n_samples = 170/862, baseline = 0.5592, acc = 0.6604
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 73, n_samples = 164/862, baseline = 0.5559, acc = 0.6619
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 74, n_samples = 162/862, baseline = 0.5414, acc = 0.6614
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 75, n_samples = 167/862, baseline = 0.5626, acc = 0.6878
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 76, n_samples = 165/862, baseline = 0.5466, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 77, n_samples = 162/862, baseline = 0.5571, acc = 0.6914
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 78, n_samples = 193/862, baseline = 0.5516, acc = 0.6771
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 79, n_samples = 159/862, baseline = 0.5605, acc = 0.6828
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 80, n_samples = 183/862, baseline = 0.5567, acc = 0.6598
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 81, n_samples = 169/862, baseline = 0.5570, acc = 0.6739
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 82, n_samples = 176/862, baseline = 0.5496, acc = 0.6691
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 83, n_samples = 161/862, baseline = 0.5492, acc = 0.6733
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 84, n_samples = 192/862, baseline = 0.5642, acc = 0.6746
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 85, n_samples = 185/862, baseline = 0.5510, acc = 0.6839
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 86, n_samples = 159/862, baseline = 0.5505, acc = 0.6757
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 87, n_samples = 207/862, baseline = 0.5664, acc = 0.6504
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 88, n_samples = 165/862, baseline = 0.5509, acc = 0.6514
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 89, n_samples = 174/862, baseline = 0.5465, acc = 0.6628
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 90, n_samples = 150/862, baseline = 0.5520, acc = 0.6742
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 91, n_samples = 153/862, baseline = 0.5529, acc = 0.6742
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 92, n_samples = 170/862, baseline = 0.5390, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 93, n_samples = 173/862, baseline = 0.5559, acc = 0.6981
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 94, n_samples = 170/862, baseline = 0.5520, acc = 0.6850
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 95, n_samples = 174/862, baseline = 0.5523, acc = 0.6642
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 96, n_samples = 171/862, baseline = 0.5630, acc = 0.6570
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 97, n_samples = 167/862, baseline = 0.5482, acc = 0.6863
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0200, intercept=True iter = 98, n_samples = 173/862, baseline = 0.5457, acc = 0.6618
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0200, intercept=True iter = 99, n_samples = 163/862, baseline = 0.5522, acc = 0.6838
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 0, n_samples = 175/862, baseline = 0.5531, acc = 0.6739
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 1, n_samples = 170/862, baseline = 0.5448, acc = 0.6720
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 2, n_samples = 191/862, baseline = 0.5440, acc = 0.6632
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 3, n_samples = 163/862, baseline = 0.5536, acc = 0.6795
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 4, n_samples = 158/862, baseline = 0.5440, acc = 0.6747
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 5, n_samples = 169/862, baseline = 0.5743, acc = 0.6465
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 6, n_samples = 180/862, baseline = 0.5601, acc = 0.6672
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 7, n_samples = 184/862, baseline = 0.5546, acc = 0.6726
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 8, n_samples = 153/862, baseline = 0.5571, acc = 0.6756
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 9, n_samples = 169/862, baseline = 0.5397, acc = 0.6753
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 10, n_samples = 181/862, baseline = 0.5536, acc = 0.6725
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 11, n_samples = 183/862, baseline = 0.5582, acc = 0.6878
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 12, n_samples = 178/862, baseline = 0.5570, acc = 0.6813
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 13, n_samples = 178/862, baseline = 0.5380, acc = 0.6623
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 14, n_samples = 183/862, baseline = 0.5611, acc = 0.6922
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 15, n_samples = 187/862, baseline = 0.5407, acc = 0.6667
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 16, n_samples = 169/862, baseline = 0.5469, acc = 0.6724
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 17, n_samples = 137/862, baseline = 0.5490, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 18, n_samples = 178/862, baseline = 0.5570, acc = 0.6769
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 19, n_samples = 163/862, baseline = 0.5680, acc = 0.6481
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 20, n_samples = 198/862, baseline = 0.5392, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 21, n_samples = 173/862, baseline = 0.5472, acc = 0.6807
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 22, n_samples = 165/862, baseline = 0.5638, acc = 0.6858
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 23, n_samples = 166/862, baseline = 0.5503, acc = 0.6825
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 24, n_samples = 179/862, baseline = 0.5417, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 25, n_samples = 187/862, baseline = 0.5496, acc = 0.6696
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 26, n_samples = 179/862, baseline = 0.5490, acc = 0.6764
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 27, n_samples = 177/862, baseline = 0.5474, acc = 0.6555
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 28, n_samples = 166/862, baseline = 0.5589, acc = 0.6868
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 29, n_samples = 172/862, baseline = 0.5536, acc = 0.6565
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 30, n_samples = 160/862, baseline = 0.5484, acc = 0.6752
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 31, n_samples = 183/862, baseline = 0.5449, acc = 0.6627
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 32, n_samples = 182/862, baseline = 0.5515, acc = 0.6779
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 33, n_samples = 148/862, baseline = 0.5364, acc = 0.6653
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 34, n_samples = 180/862, baseline = 0.5513, acc = 0.6818
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 35, n_samples = 188/862, baseline = 0.5490, acc = 0.6825
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 36, n_samples = 182/862, baseline = 0.5544, acc = 0.6868
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 37, n_samples = 176/862, baseline = 0.5394, acc = 0.6647
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 38, n_samples = 178/862, baseline = 0.5526, acc = 0.6652
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 39, n_samples = 169/862, baseline = 0.5584, acc = 0.6869
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 40, n_samples = 165/862, baseline = 0.5567, acc = 0.6657
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 41, n_samples = 150/862, baseline = 0.5520, acc = 0.6868
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 42, n_samples = 190/862, baseline = 0.5461, acc = 0.6771
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 43, n_samples = 167/862, baseline = 0.5525, acc = 0.6734
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 44, n_samples = 165/862, baseline = 0.5395, acc = 0.6628
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 45, n_samples = 168/862, baseline = 0.5476, acc = 0.6859
-- ['Age', u'Max D', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 46, n_samples = 161/862, baseline = 0.5407, acc = 0.6548
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 47, n_samples = 172/862, baseline = 0.5565, acc = 0.6812
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 48, n_samples = 192/862, baseline = 0.5537, acc = 0.6731
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 49, n_samples = 190/862, baseline = 0.5655, acc = 0.6548
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 50, n_samples = 198/862, baseline = 0.5497, acc = 0.6792
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 51, n_samples = 171/862, baseline = 0.5456, acc = 0.6686
-- [u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 52, n_samples = 185/862, baseline = 0.5510, acc = 0.6987
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 53, n_samples = 165/862, baseline = 0.5438, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 54, n_samples = 175/862, baseline = 0.5546, acc = 0.6856
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 55, n_samples = 184/862, baseline = 0.5634, acc = 0.6696
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 56, n_samples = 158/862, baseline = 0.5483, acc = 0.6747
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 57, n_samples = 174/862, baseline = 0.5567, acc = 0.6919
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 58, n_samples = 201/862, baseline = 0.5507, acc = 0.6717
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 59, n_samples = 183/862, baseline = 0.5493, acc = 0.6760
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 60, n_samples = 190/862, baseline = 0.5491, acc = 0.6905
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 61, n_samples = 195/862, baseline = 0.5337, acc = 0.6672
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 62, n_samples = 191/862, baseline = 0.5559, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 63, n_samples = 191/862, baseline = 0.5484, acc = 0.6781
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 64, n_samples = 169/862, baseline = 0.5483, acc = 0.6724
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 65, n_samples = 182/862, baseline = 0.5485, acc = 0.6882
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 66, n_samples = 158/862, baseline = 0.5483, acc = 0.6847
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 67, n_samples = 176/862, baseline = 0.5423, acc = 0.6691
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 68, n_samples = 154/862, baseline = 0.5607, acc = 0.6653
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 69, n_samples = 174/862, baseline = 0.5596, acc = 0.6730
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 70, n_samples = 164/862, baseline = 0.5774, acc = 0.6633
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 71, n_samples = 182/862, baseline = 0.5324, acc = 0.6662
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 72, n_samples = 178/862, baseline = 0.5658, acc = 0.6798
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 73, n_samples = 178/862, baseline = 0.5541, acc = 0.6886
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 74, n_samples = 159/862, baseline = 0.5633, acc = 0.6771
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 75, n_samples = 180/862, baseline = 0.5630, acc = 0.6833
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 76, n_samples = 181/862, baseline = 0.5448, acc = 0.6769
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 77, n_samples = 163/862, baseline = 0.5379, acc = 0.6495
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 78, n_samples = 163/862, baseline = 0.5494, acc = 0.6581
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 79, n_samples = 174/862, baseline = 0.5422, acc = 0.6686
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 80, n_samples = 176/862, baseline = 0.5496, acc = 0.6880
-- [u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 81, n_samples = 144/862, baseline = 0.5585, acc = 0.6713
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 82, n_samples = 176/862, baseline = 0.5466, acc = 0.6297
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 83, n_samples = 170/862, baseline = 0.5578, acc = 0.6676
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 84, n_samples = 152/862, baseline = 0.5592, acc = 0.6648
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 85, n_samples = 176/862, baseline = 0.5394, acc = 0.6647
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 86, n_samples = 176/862, baseline = 0.5569, acc = 0.6647
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 87, n_samples = 185/862, baseline = 0.5465, acc = 0.6558
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 88, n_samples = 192/862, baseline = 0.5448, acc = 0.6687
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0400, intercept=True iter = 89, n_samples = 145/862, baseline = 0.5537, acc = 0.6862
-- ['Age', u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 90, n_samples = 174/862, baseline = 0.5581, acc = 0.6788
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 91, n_samples = 172/862, baseline = 0.5507, acc = 0.6855
-- [u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 92, n_samples = 174/862, baseline = 0.5392, acc = 0.6788
-- [u'Max D', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 93, n_samples = 170/862, baseline = 0.5462, acc = 0.6777
-- ['Age', u'Max D', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 94, n_samples = 170/862, baseline = 0.5506, acc = 0.6749
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 95, n_samples = 182/862, baseline = 0.5529, acc = 0.6779
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 96, n_samples = 181/862, baseline = 0.5477, acc = 0.6843
-- [u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 97, n_samples = 172/862, baseline = 0.5493, acc = 0.6754
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0400, intercept=True iter = 98, n_samples = 169/862, baseline = 0.5440, acc = 0.6999
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0400, intercept=True iter = 99, n_samples = 172/862, baseline = 0.5565, acc = 0.6826
-- [u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 0, n_samples = 158/862, baseline = 0.5540, acc = 0.7017
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 1, n_samples = 171/862, baseline = 0.5456, acc = 0.6570
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 2, n_samples = 166/862, baseline = 0.5647, acc = 0.6552
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 3, n_samples = 170/862, baseline = 0.5231, acc = 0.6460
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 4, n_samples = 174/862, baseline = 0.5581, acc = 0.6846
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 5, n_samples = 175/862, baseline = 0.5269, acc = 0.6230
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 6, n_samples = 171/862, baseline = 0.5499, acc = 0.6961
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 7, n_samples = 159/862, baseline = 0.5647, acc = 0.6757
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 8, n_samples = 162/862, baseline = 0.5371, acc = 0.6814
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 9, n_samples = 192/862, baseline = 0.5567, acc = 0.6761
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 10, n_samples = 152/862, baseline = 0.5521, acc = 0.6930
-- [u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 11, n_samples = 190/862, baseline = 0.5446, acc = 0.6875
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 12, n_samples = 173/862, baseline = 0.5486, acc = 0.6778
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 13, n_samples = 201/862, baseline = 0.5537, acc = 0.6732
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 14, n_samples = 185/862, baseline = 0.5510, acc = 0.6795
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 15, n_samples = 163/862, baseline = 0.5565, acc = 0.6924
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 16, n_samples = 161/862, baseline = 0.5521, acc = 0.6890
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 17, n_samples = 173/862, baseline = 0.5399, acc = 0.6546
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 18, n_samples = 145/862, baseline = 0.5356, acc = 0.6750
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 19, n_samples = 165/862, baseline = 0.5495, acc = 0.6714
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 20, n_samples = 194/862, baseline = 0.5494, acc = 0.6841
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 21, n_samples = 169/862, baseline = 0.5440, acc = 0.6681
-- ['Age', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 22, n_samples = 180/862, baseline = 0.5484, acc = 0.6921
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 23, n_samples = 162/862, baseline = 0.5543, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 24, n_samples = 164/862, baseline = 0.5616, acc = 0.6734
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 25, n_samples = 171/862, baseline = 0.5499, acc = 0.6700
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 26, n_samples = 171/862, baseline = 0.5499, acc = 0.6860
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 27, n_samples = 167/862, baseline = 0.5453, acc = 0.6849
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 28, n_samples = 179/862, baseline = 0.5578, acc = 0.6837
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 29, n_samples = 161/862, baseline = 0.5549, acc = 0.6676
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 30, n_samples = 160/862, baseline = 0.5456, acc = 0.6795
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 31, n_samples = 180/862, baseline = 0.5513, acc = 0.6745
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 32, n_samples = 161/862, baseline = 0.5578, acc = 0.6619
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 33, n_samples = 194/862, baseline = 0.5494, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 34, n_samples = 178/862, baseline = 0.5687, acc = 0.6769
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 35, n_samples = 160/862, baseline = 0.5627, acc = 0.6966
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 36, n_samples = 175/862, baseline = 0.5459, acc = 0.6914
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 37, n_samples = 176/862, baseline = 0.5452, acc = 0.6531
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 38, n_samples = 172/862, baseline = 0.5580, acc = 0.6768
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 39, n_samples = 178/862, baseline = 0.5614, acc = 0.6711
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 40, n_samples = 181/862, baseline = 0.5389, acc = 0.6872
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 41, n_samples = 189/862, baseline = 0.5646, acc = 0.6790
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 42, n_samples = 173/862, baseline = 0.5675, acc = 0.6589
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 43, n_samples = 195/862, baseline = 0.5622, acc = 0.6897
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 44, n_samples = 165/862, baseline = 0.5538, acc = 0.6958
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 45, n_samples = 166/862, baseline = 0.5431, acc = 0.6782
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 46, n_samples = 154/862, baseline = 0.5508, acc = 0.6935
-- [u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 47, n_samples = 195/862, baseline = 0.5412, acc = 0.6762
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 48, n_samples = 172/862, baseline = 0.5536, acc = 0.6739
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 49, n_samples = 161/862, baseline = 0.5478, acc = 0.6819
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 50, n_samples = 181/862, baseline = 0.5492, acc = 0.6725
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 51, n_samples = 198/862, baseline = 0.5331, acc = 0.6717
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 52, n_samples = 167/862, baseline = 0.5410, acc = 0.6921
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 53, n_samples = 183/862, baseline = 0.5523, acc = 0.6745
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 54, n_samples = 167/862, baseline = 0.5525, acc = 0.6518
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 55, n_samples = 161/862, baseline = 0.5464, acc = 0.6790
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 56, n_samples = 171/862, baseline = 0.5485, acc = 0.6961
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 57, n_samples = 165/862, baseline = 0.5466, acc = 0.6628
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 58, n_samples = 191/862, baseline = 0.5589, acc = 0.6841
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 59, n_samples = 194/862, baseline = 0.5509, acc = 0.6722
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 60, n_samples = 160/862, baseline = 0.5484, acc = 0.6781
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 61, n_samples = 180/862, baseline = 0.5499, acc = 0.6804
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 62, n_samples = 170/862, baseline = 0.5462, acc = 0.6864
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 63, n_samples = 180/862, baseline = 0.5440, acc = 0.6701
-- ['Age', u'Max D', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 64, n_samples = 165/862, baseline = 0.5552, acc = 0.7016
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 65, n_samples = 167/862, baseline = 0.5568, acc = 0.6719
-- ['Age', u'SM', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 66, n_samples = 166/862, baseline = 0.5589, acc = 0.6624
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 67, n_samples = 167/862, baseline = 0.5453, acc = 0.6734
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 68, n_samples = 173/862, baseline = 0.5501, acc = 0.6894
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 69, n_samples = 183/862, baseline = 0.5538, acc = 0.6804
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 70, n_samples = 156/862, baseline = 0.5623, acc = 0.6827
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 71, n_samples = 169/862, baseline = 0.5584, acc = 0.6710
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 72, n_samples = 191/862, baseline = 0.5559, acc = 0.6811
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 73, n_samples = 171/862, baseline = 0.5572, acc = 0.6643
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 74, n_samples = 188/862, baseline = 0.5653, acc = 0.6973
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 75, n_samples = 192/862, baseline = 0.5537, acc = 0.6687
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 76, n_samples = 185/862, baseline = 0.5569, acc = 0.6750
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 77, n_samples = 165/862, baseline = 0.5438, acc = 0.6758
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 78, n_samples = 175/862, baseline = 0.5531, acc = 0.6739
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 79, n_samples = 172/862, baseline = 0.5449, acc = 0.6739
-- ['Age', u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 80, n_samples = 156/862, baseline = 0.5524, acc = 0.6969
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 81, n_samples = 155/862, baseline = 0.5502, acc = 0.6676
-- ['Age', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 82, n_samples = 180/862, baseline = 0.5323, acc = 0.6657
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 83, n_samples = 187/862, baseline = 0.5452, acc = 0.6741
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 84, n_samples = 168/862, baseline = 0.5490, acc = 0.6931
-- ['Age', u'SM', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 85, n_samples = 163/862, baseline = 0.5479, acc = 0.6753
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 86, n_samples = 182/862, baseline = 0.5588, acc = 0.6515
-- ['Age', u'SM', u'Max D', 'Max_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 87, n_samples = 158/862, baseline = 0.5511, acc = 0.6705
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 88, n_samples = 182/862, baseline = 0.5456, acc = 0.7015
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 89, n_samples = 185/862, baseline = 0.5598, acc = 0.6928
-- ['Age', u'Max D', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 90, n_samples = 179/862, baseline = 0.5520, acc = 0.6867
-- ['Age', u'Max D', 'Volume', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 91, n_samples = 172/862, baseline = 0.5507, acc = 0.6551
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 92, n_samples = 168/862, baseline = 0.5706, acc = 0.6499
-- ['Age', u'Max D', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 93, n_samples = 161/862, baseline = 0.5464, acc = 0.6805
-- ['Age', u'Max D', 'Volume', 'Max_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 94, n_samples = 189/862, baseline = 0.5572, acc = 0.6731
-- ['Age', u'Max D', 'Volume', 'Max_Dose']
LogisticRegression: C = 0.0800, intercept=True iter = 95, n_samples = 157/862, baseline = 0.5532, acc = 0.6780
-- ['Age', u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose', u'Shots']
LogisticRegression: C = 0.0800, intercept=True iter = 96, n_samples = 181/862, baseline = 0.5345, acc = 0.6579
-- [u'Max D', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 97, n_samples = 163/862, baseline = 0.5536, acc = 0.6896
-- [u'Max D', 'Volume', 'Number_Draining_Veins', 'Max_Dose', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 98, n_samples = 162/862, baseline = 0.5500, acc = 0.6800
-- ['Age', u'Max D', 'Volume', 'Marginal_Dose', u'Isodose']
LogisticRegression: C = 0.0800, intercept=True iter = 99, n_samples = 178/862, baseline = 0.5453, acc = 0.6871
-- [u'Max D', 'Marginal_Dose']
Counter({u'Max D': 3595, 'Max_Dose': 3251, 'Age': 3116, u'Isodose': 2795, 'Volume': 2683, 'Marginal_Dose': 2403, 'Number_Draining_Veins': 1467, u'Shots': 1313, u'Embo': 1041, u'SM': 685, 'Early_RIC': 468, 'Aneurysm': 389, u'Draining_Vein_Depth': 233, u'Sex': 212, 'History_of_Hemorrhage': 122})
In [14]:
np.save("X.npy", X)
In [15]:
np.save("Y_RIC.npy", Y_RIC)
In [242]:
np.save("Y_Obliteration", Y_Obliteration)
In [ ]:
"""
grid_search = GridSearchCV(Normalizer(model), param_grids, scoring=roc_auc)
grid_search.fit(X_train, Y_train)
print "Best model", grid_search.best_estimator_
print "Best model score", grid_search.best_score_
print
curr_models[model] = grid_search.best_estimator_
# shuffle the training indices since they come to us sorted
np.random.shuffle(train_idx)
# split the training indices into model fitting/validation
# subsets
model_selection_idx = train_idx[:-n_cv_validation]
model_validation_idx = train_idx[-n_cv_validation:]
print " -- # selection train", len(model_selection_idx)
print " -- # validation train", len(model_validation_idx)
print "-- Using subset of training to choose best model"
X_model_selection = X[model_selection_idx, :]
Y_model_selection = Y_Obliteration[model_selection_idx]
print "Y_model_selection.shape", Y_model_selection.shape, Y_model_selection.dtype
X_model_validation = X[model_validation_idx, :]
Y_model_validation = Y_Obliteration[model_validation_idx]
print "Y_model_validation", Y_model_validation.shape, Y_model_validation.dtype
# normalize model selection & validation features
X_model_selection, X_model_validation = normalize(X_model_selection, X_model_validation)
best_auc = 0
best_model = None
for model in models:
print
print model
model.fit(X_model_selection, Y_model_selection)
pred = class_prob(model, X_model_validation)
auc = roc_auc_score(Y_model_validation, pred)
print "--", auc
if auc > best_auc:
best_auc = auc
best_model = model
# once we've found the best model via selection/validation sets
# evaluate it on the held-out CV test set
X_train, X_test = normalize(X_train, X_test)
best_model.fit(X_train, Y_train)
pred = class_prob(best_model, X_test)
cv_auc = roc_auc_score(Y_test, pred)
print
print "== Best model in CV-fold:"
print best_model
print "== CV-fold AUC score for best model:", cv_auc
cv_aucs.append(cv_auc)
best_models.append(best_model)
for model, auc in zip(best_models, cv_aucs):
print model
print auc
print
print cv_aucs
print "mean AUC", np.mean(cv_aucs)
print "median AUC", np.median(cv_aucs)
print "std AUC", np.std(cv_aucs)
"""
Content source: hammerlab/avm
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