$\tt{Accuracy} = \frac{correct}{samples}$



In [24]:

    
from sklearn import datasets, neighbors, metrics
import pandas as pd
%matplotlib inline



In [6]:

    
iris = datasets.load_iris()



In [8]:

    
irisdf = pd.DataFrame(iris.data, columns=iris.feature_names)
irisdf.head(4)









    Out[8]:






  
    
      
      sepal length (cm)
      sepal width (cm)
      petal length (cm)
      petal width (cm)
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
    
    
      1
      4.9
      3.0
      1.4
      0.2
    
    
      2
      4.7
      3.2
      1.3
      0.2
    
    
      3
      4.6
      3.1
      1.5
      0.2



In [9]:

    
irisdf["target"] = iris.target



In [10]:

    
irisdf.head(4)









    Out[10]:






  
    
      
      sepal length (cm)
      sepal width (cm)
      petal length (cm)
      petal width (cm)
      target
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      0
    
    
      1
      4.9
      3.0
      1.4
      0.2
      0
    
    
      2
      4.7
      3.2
      1.3
      0.2
      0
    
    
      3
      4.6
      3.1
      1.5
      0.2
      0



In [12]:

    
cmap = {"0": "r", "1": "g", "2": "b"}



In [13]:

    
irisdf["ctarget"] = irisdf.target.apply(lambda x: cmap[str(x)])



In [14]:

    
irisdf.head(6)









    Out[14]:






  
    
      
      sepal length (cm)
      sepal width (cm)
      petal length (cm)
      petal width (cm)
      target
      ctarget
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      0
      r
    
    
      1
      4.9
      3.0
      1.4
      0.2
      0
      r
    
    
      2
      4.7
      3.2
      1.3
      0.2
      0
      r
    
    
      3
      4.6
      3.1
      1.5
      0.2
      0
      r
    
    
      4
      5.0
      3.6
      1.4
      0.2
      0
      r
    
    
      5
      5.4
      3.9
      1.7
      0.4
      0
      r



In [17]:

    
irisdf.plot('petal length (cm)', 'petal width (cm)', kind='scatter', c=irisdf.ctarget)









    Out[17]:





<matplotlib.axes._subplots.AxesSubplot at 0x1139f0978>



In [19]:

    
print(irisdf.describe())









    



       sepal length (cm)  sepal width (cm)  petal length (cm)  \
count         150.000000        150.000000         150.000000   
mean            5.843333          3.054000           3.758667   
std             0.828066          0.433594           1.764420   
min             4.300000          2.000000           1.000000   
25%             5.100000          2.800000           1.600000   
50%             5.800000          3.000000           4.350000   
75%             6.400000          3.300000           5.100000   
max             7.900000          4.400000           6.900000   

       petal width (cm)      target  
count        150.000000  150.000000  
mean           1.198667    1.000000  
std            0.763161    0.819232  
min            0.100000    0.000000  
25%            0.300000    0.000000  
50%            1.300000    1.000000  
75%            1.800000    2.000000  
max            2.500000    2.000000



In [25]:

    
def my_classifier(row):
    if row["petal length (cm)"] < 2:
        return 0
    else:
        return 1

irisdf['predictions_1'] = irisdf.apply(my_classifier, axis=1)



In [26]:

    
irisdf.head(6)









    Out[26]:






  
    
      
      sepal length (cm)
      sepal width (cm)
      petal length (cm)
      petal width (cm)
      target
      ctarget
      predictions_1
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      0
      r
      0
    
    
      1
      4.9
      3.0
      1.4
      0.2
      0
      r
      0
    
    
      2
      4.7
      3.2
      1.3
      0.2
      0
      r
      0
    
    
      3
      4.6
      3.1
      1.5
      0.2
      0
      r
      0
    
    
      4
      5.0
      3.6
      1.4
      0.2
      0
      r
      0
    
    
      5
      5.4
      3.9
      1.7
      0.4
      0
      r
      0

Plot decision boundaries



In [ ]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	ctarget
0	5.1	3.5	1.4	0.2	r
1	4.9	3.0	1.4	0.2	r
2	4.7	3.2	1.3	0.2	r
3	4.6	3.1	1.5	0.2	r
4	5.0	3.6	1.4	0.2	r
5	5.4	3.9	1.7	0.4	r

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	ctarget
0	5.1	3.5	1.4	0.2	r
1	4.9	3.0	1.4	0.2	r
2	4.7	3.2	1.3	0.2	r
3	4.6	3.1	1.5	0.2	r
4	5.0	3.6	1.4	0.2	r
5	5.4	3.9	1.7	0.4	r

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	ctarget
0	5.1	3.5	1.4	0.2	r
1	4.9	3.0	1.4	0.2	r
2	4.7	3.2	1.3	0.2	r
3	4.6	3.1	1.5	0.2	r
4	5.0	3.6	1.4	0.2	r
5	5.4	3.9	1.7	0.4	r

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	ctarget
0	5.1	3.5	1.4	0.2	r
1	4.9	3.0	1.4	0.2	r
2	4.7	3.2	1.3	0.2	r
3	4.6	3.1	1.5	0.2	r
4	5.0	3.6	1.4	0.2	r
5	5.4	3.9	1.7	0.4	r

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	ctarget
0	5.1	3.5	1.4	0.2	r
1	4.9	3.0	1.4	0.2	r
2	4.7	3.2	1.3	0.2	r
3	4.6	3.1	1.5	0.2	r
4	5.0	3.6	1.4	0.2	r
5	5.4	3.9	1.7	0.4	r

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	ctarget
0	5.1	3.5	1.4	0.2	r
1	4.9	3.0	1.4	0.2	r
2	4.7	3.2	1.3	0.2	r
3	4.6	3.1	1.5	0.2	r
4	5.0	3.6	1.4	0.2	r
5	5.4	3.9	1.7	0.4	r