In [1]:
#importing scikit-learn datasets package and the Logistic Regression package
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
#reading the iris dataset from the datasets package
iris = datasets.load_iris()
iris.data.shape,iris.target.shape
#implementing the methods of Logistic Regression
m=LogisticRegression()
#printing the labels, feature names and their description
print(iris.target,iris.target_names,iris.feature_names,iris.DESCR)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2] ['setosa' 'versicolor' 'virginica'] ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] Iris Plants Database
====================
Notes
-----
Data Set Characteristics:
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
:Summary Statistics:
============== ==== ==== ======= ===== ====================
Min Max Mean SD Class Correlation
============== ==== ==== ======= ===== ====================
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
============== ==== ==== ======= ===== ====================
:Missing Attribute Values: None
:Class Distribution: 33.3% for each of 3 classes.
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris
The famous Iris database, first used by Sir R.A Fisher
This is perhaps the best known database to be found in the
pattern recognition literature. Fisher's paper is a classic in the field and
is referenced frequently to this day. (See Duda & Hart, for example.) The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant. One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.
References
----------
- Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
Mathematical Statistics" (John Wiley, NY, 1950).
- Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
on Information Theory, May 1972, 431-433.
- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
- Many, many more ...
In [2]:
#viewing the dataset
print(iris.data)
[[ 5.1 3.5 1.4 0.2]
[ 4.9 3. 1.4 0.2]
[ 4.7 3.2 1.3 0.2]
[ 4.6 3.1 1.5 0.2]
[ 5. 3.6 1.4 0.2]
[ 5.4 3.9 1.7 0.4]
[ 4.6 3.4 1.4 0.3]
[ 5. 3.4 1.5 0.2]
[ 4.4 2.9 1.4 0.2]
[ 4.9 3.1 1.5 0.1]
[ 5.4 3.7 1.5 0.2]
[ 4.8 3.4 1.6 0.2]
[ 4.8 3. 1.4 0.1]
[ 4.3 3. 1.1 0.1]
[ 5.8 4. 1.2 0.2]
[ 5.7 4.4 1.5 0.4]
[ 5.4 3.9 1.3 0.4]
[ 5.1 3.5 1.4 0.3]
[ 5.7 3.8 1.7 0.3]
[ 5.1 3.8 1.5 0.3]
[ 5.4 3.4 1.7 0.2]
[ 5.1 3.7 1.5 0.4]
[ 4.6 3.6 1. 0.2]
[ 5.1 3.3 1.7 0.5]
[ 4.8 3.4 1.9 0.2]
[ 5. 3. 1.6 0.2]
[ 5. 3.4 1.6 0.4]
[ 5.2 3.5 1.5 0.2]
[ 5.2 3.4 1.4 0.2]
[ 4.7 3.2 1.6 0.2]
[ 4.8 3.1 1.6 0.2]
[ 5.4 3.4 1.5 0.4]
[ 5.2 4.1 1.5 0.1]
[ 5.5 4.2 1.4 0.2]
[ 4.9 3.1 1.5 0.1]
[ 5. 3.2 1.2 0.2]
[ 5.5 3.5 1.3 0.2]
[ 4.9 3.1 1.5 0.1]
[ 4.4 3. 1.3 0.2]
[ 5.1 3.4 1.5 0.2]
[ 5. 3.5 1.3 0.3]
[ 4.5 2.3 1.3 0.3]
[ 4.4 3.2 1.3 0.2]
[ 5. 3.5 1.6 0.6]
[ 5.1 3.8 1.9 0.4]
[ 4.8 3. 1.4 0.3]
[ 5.1 3.8 1.6 0.2]
[ 4.6 3.2 1.4 0.2]
[ 5.3 3.7 1.5 0.2]
[ 5. 3.3 1.4 0.2]
[ 7. 3.2 4.7 1.4]
[ 6.4 3.2 4.5 1.5]
[ 6.9 3.1 4.9 1.5]
[ 5.5 2.3 4. 1.3]
[ 6.5 2.8 4.6 1.5]
[ 5.7 2.8 4.5 1.3]
[ 6.3 3.3 4.7 1.6]
[ 4.9 2.4 3.3 1. ]
[ 6.6 2.9 4.6 1.3]
[ 5.2 2.7 3.9 1.4]
[ 5. 2. 3.5 1. ]
[ 5.9 3. 4.2 1.5]
[ 6. 2.2 4. 1. ]
[ 6.1 2.9 4.7 1.4]
[ 5.6 2.9 3.6 1.3]
[ 6.7 3.1 4.4 1.4]
[ 5.6 3. 4.5 1.5]
[ 5.8 2.7 4.1 1. ]
[ 6.2 2.2 4.5 1.5]
[ 5.6 2.5 3.9 1.1]
[ 5.9 3.2 4.8 1.8]
[ 6.1 2.8 4. 1.3]
[ 6.3 2.5 4.9 1.5]
[ 6.1 2.8 4.7 1.2]
[ 6.4 2.9 4.3 1.3]
[ 6.6 3. 4.4 1.4]
[ 6.8 2.8 4.8 1.4]
[ 6.7 3. 5. 1.7]
[ 6. 2.9 4.5 1.5]
[ 5.7 2.6 3.5 1. ]
[ 5.5 2.4 3.8 1.1]
[ 5.5 2.4 3.7 1. ]
[ 5.8 2.7 3.9 1.2]
[ 6. 2.7 5.1 1.6]
[ 5.4 3. 4.5 1.5]
[ 6. 3.4 4.5 1.6]
[ 6.7 3.1 4.7 1.5]
[ 6.3 2.3 4.4 1.3]
[ 5.6 3. 4.1 1.3]
[ 5.5 2.5 4. 1.3]
[ 5.5 2.6 4.4 1.2]
[ 6.1 3. 4.6 1.4]
[ 5.8 2.6 4. 1.2]
[ 5. 2.3 3.3 1. ]
[ 5.6 2.7 4.2 1.3]
[ 5.7 3. 4.2 1.2]
[ 5.7 2.9 4.2 1.3]
[ 6.2 2.9 4.3 1.3]
[ 5.1 2.5 3. 1.1]
[ 5.7 2.8 4.1 1.3]
[ 6.3 3.3 6. 2.5]
[ 5.8 2.7 5.1 1.9]
[ 7.1 3. 5.9 2.1]
[ 6.3 2.9 5.6 1.8]
[ 6.5 3. 5.8 2.2]
[ 7.6 3. 6.6 2.1]
[ 4.9 2.5 4.5 1.7]
[ 7.3 2.9 6.3 1.8]
[ 6.7 2.5 5.8 1.8]
[ 7.2 3.6 6.1 2.5]
[ 6.5 3.2 5.1 2. ]
[ 6.4 2.7 5.3 1.9]
[ 6.8 3. 5.5 2.1]
[ 5.7 2.5 5. 2. ]
[ 5.8 2.8 5.1 2.4]
[ 6.4 3.2 5.3 2.3]
[ 6.5 3. 5.5 1.8]
[ 7.7 3.8 6.7 2.2]
[ 7.7 2.6 6.9 2.3]
[ 6. 2.2 5. 1.5]
[ 6.9 3.2 5.7 2.3]
[ 5.6 2.8 4.9 2. ]
[ 7.7 2.8 6.7 2. ]
[ 6.3 2.7 4.9 1.8]
[ 6.7 3.3 5.7 2.1]
[ 7.2 3.2 6. 1.8]
[ 6.2 2.8 4.8 1.8]
[ 6.1 3. 4.9 1.8]
[ 6.4 2.8 5.6 2.1]
[ 7.2 3. 5.8 1.6]
[ 7.4 2.8 6.1 1.9]
[ 7.9 3.8 6.4 2. ]
[ 6.4 2.8 5.6 2.2]
[ 6.3 2.8 5.1 1.5]
[ 6.1 2.6 5.6 1.4]
[ 7.7 3. 6.1 2.3]
[ 6.3 3.4 5.6 2.4]
[ 6.4 3.1 5.5 1.8]
[ 6. 3. 4.8 1.8]
[ 6.9 3.1 5.4 2.1]
[ 6.7 3.1 5.6 2.4]
[ 6.9 3.1 5.1 2.3]
[ 5.8 2.7 5.1 1.9]
[ 6.8 3.2 5.9 2.3]
[ 6.7 3.3 5.7 2.5]
[ 6.7 3. 5.2 2.3]
[ 6.3 2.5 5. 1.9]
[ 6.5 3. 5.2 2. ]
[ 6.2 3.4 5.4 2.3]
[ 5.9 3. 5.1 1.8]]
In [3]:
#viewing the dataset
print(iris.target)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
In [4]:
#fit - fit the model according to the given training data
m.fit(iris.data,iris.target)
Out[4]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
In [5]:
#decision_function - to predict confidence scores (signed distance of that sample to the hyperplane) for examples.
m.decision_function(iris.data)
Out[5]:
array([[ 4.12377083e+00, -1.86101702e+00, -1.13225953e+01],
[ 3.31012447e+00, -1.14392836e+00, -1.02139561e+01],
[ 3.74560040e+00, -1.60518870e+00, -1.04264019e+01],
[ 3.10554360e+00, -1.37123782e+00, -9.60802820e+00],
[ 4.22840174e+00, -2.06276430e+00, -1.13052696e+01],
[ 3.94832492e+00, -2.48016878e+00, -1.11961922e+01],
[ 3.66723742e+00, -2.04780738e+00, -1.00598677e+01],
[ 3.70992814e+00, -1.68483190e+00, -1.07513188e+01],
[ 2.95650057e+00, -1.19216488e+00, -9.20676667e+00],
[ 3.33294961e+00, -1.10769207e+00, -1.03758240e+01],
[ 4.31431269e+00, -1.99842598e+00, -1.18946093e+01],
[ 3.40071636e+00, -1.71039408e+00, -1.01627166e+01],
[ 3.37153515e+00, -1.04703849e+00, -1.02987418e+01],
[ 3.84268334e+00, -1.42865562e+00, -1.01862707e+01],
[ 5.59733959e+00, -2.48531735e+00, -1.37791914e+01],
[ 5.25589834e+00, -3.27112499e+00, -1.29697783e+01],
[ 4.85318139e+00, -2.71123183e+00, -1.21845809e+01],
[ 4.02086132e+00, -1.99957086e+00, -1.10670571e+01],
[ 4.02960118e+00, -2.05653971e+00, -1.18105612e+01],
[ 4.23303642e+00, -2.42205505e+00, -1.12802404e+01],
[ 3.42349524e+00, -1.40264450e+00, -1.09401345e+01],
[ 3.98399717e+00, -2.40052557e+00, -1.08712754e+01],
[ 4.96726288e+00, -2.46048323e+00, -1.16106482e+01],
[ 2.84414047e+00, -1.78321462e+00, -9.50783547e+00],
[ 2.72207401e+00, -1.53709679e+00, -9.42142506e+00],
[ 2.89919507e+00, -9.86732865e-01, -9.89051426e+00],
[ 3.27789501e+00, -1.90417383e+00, -9.99314517e+00],
[ 3.93905555e+00, -1.76158728e+00, -1.12462506e+01],
[ 4.01913993e+00, -1.65926973e+00, -1.13399210e+01],
[ 3.06695805e+00, -1.43189141e+00, -9.68511038e+00],
[ 2.96232714e+00, -1.23014412e+00, -9.70243606e+00],
[ 3.67010446e+00, -1.79528371e+00, -1.09232524e+01],
[ 4.91874349e+00, -2.58353335e+00, -1.24223498e+01],
[ 5.31267434e+00, -2.81494437e+00, -1.30795932e+01],
[ 3.33294961e+00, -1.10769207e+00, -1.03758240e+01],
[ 4.09631102e+00, -1.53796255e+00, -1.11857566e+01],
[ 4.51598028e+00, -1.75212690e+00, -1.22527025e+01],
[ 3.33294961e+00, -1.10769207e+00, -1.03758240e+01],
[ 3.32884443e+00, -1.41001397e+00, -9.60729067e+00],
[ 3.75142698e+00, -1.64316793e+00, -1.09220713e+01],
[ 4.20557661e+00, -2.09900059e+00, -1.11434017e+01],
[ 2.24452558e+00, -3.86320610e-01, -8.44851713e+00],
[ 3.62110391e+00, -1.73018060e+00, -9.91414434e+00],
[ 3.21820573e+00, -2.34136483e+00, -9.63549558e+00],
[ 3.22527044e+00, -2.32954584e+00, -1.00363135e+01],
[ 3.16571613e+00, -1.32414617e+00, -9.78766535e+00],
[ 4.10973182e+00, -2.22573544e+00, -1.12886814e+01],
[ 3.47788745e+00, -1.58908690e+00, -1.00085522e+01],
[ 4.27281386e+00, -2.04008995e+00, -1.17238568e+01],
[ 3.79001252e+00, -1.58251435e+00, -1.08449891e+01],
[ -4.22612056e+00, -3.45527597e-01, -2.88594749e+00],
[ -4.12559483e+00, -8.49596777e-01, -2.10008852e+00],
[ -4.96908688e+00, -2.50130564e-01, -1.81203559e+00],
[ -4.47736237e+00, 2.04456245e-01, -9.29036636e-01],
[ -4.89482907e+00, -1.09833771e-01, -1.41003653e+00],
[ -4.79478660e+00, -2.23803597e-01, -8.02189996e-01],
[ -4.57630167e+00, -1.07436638e+00, -1.33303029e+00],
[ -2.68799827e+00, -1.94309697e-01, -2.55424319e+00],
[ -4.50138148e+00, 4.88545647e-02, -2.24529230e+00],
[ -3.89403531e+00, -7.57188541e-01, -1.02204538e+00],
[ -3.68344663e+00, 6.03219072e-01, -1.61709403e+00],
[ -3.94670612e+00, -9.11047271e-01, -1.68076378e+00],
[ -4.10726942e+00, 9.88520934e-01, -2.39598701e+00],
[ -5.03799927e+00, -2.40253358e-01, -8.88894345e-01],
[ -2.65422863e+00, -9.45442750e-01, -3.00873883e+00],
[ -3.81810445e+00, -4.83733472e-01, -2.96155461e+00],
[ -4.74984497e+00, -8.62741888e-01, -4.27214727e-01],
[ -3.68583250e+00, 1.62542168e-01, -2.57451898e+00],
[ -5.66988989e+00, 6.67908472e-01, -2.24315147e-01],
[ -3.71157092e+00, 1.45295499e-01, -2.16481641e+00],
[ -5.32045988e+00, -1.30028086e+00, 2.61580197e-01],
[ -3.49772068e+00, -3.45976537e-01, -2.72068590e+00],
[ -6.09485831e+00, 4.60385536e-01, 1.33040508e-01],
[ -4.97830999e+00, 1.96937646e-01, -1.24654393e+00],
[ -3.90573680e+00, -2.07770661e-01, -2.64507877e+00],
[ -4.00573302e+00, -3.65314122e-01, -2.63737526e+00],
[ -5.11985130e+00, 2.69243503e-01, -1.68363795e+00],
[ -5.63024742e+00, -3.92717105e-01, -5.58930133e-01],
[ -4.72997938e+00, -5.36002695e-01, -9.56797954e-01],
[ -2.51617637e+00, -6.56330597e-02, -3.73292264e+00],
[ -3.67298538e+00, 2.05949086e-01, -2.08773423e+00],
[ -3.34386175e+00, 2.86737166e-01, -2.59036961e+00],
[ -3.43922329e+00, -2.30097044e-01, -2.55763690e+00],
[ -6.48243308e+00, -7.79532384e-03, 1.08817693e+00],
[ -4.83284264e+00, -9.46069825e-01, -8.57096957e-02],
[ -4.10224020e+00, -1.47497313e+00, -1.46839391e+00],
[ -4.59965631e+00, -4.48990027e-01, -1.96472489e+00],
[ -5.05022818e+00, 7.68831044e-01, -1.30666809e+00],
[ -3.63916948e+00, -8.16697254e-01, -1.92667982e+00],
[ -4.18510289e+00, -1.15710393e-01, -1.23589030e+00],
[ -4.84092011e+00, 9.38231833e-02, -6.56466677e-01],
[ -4.66565542e+00, -4.58102439e-01, -1.28941835e+00],
[ -3.81156715e+00, -1.22479625e-02, -2.15711290e+00],
[ -2.79262918e+00, 7.43759025e-03, -2.57156887e+00],
[ -4.30377282e+00, -2.78681535e-01, -1.21930215e+00],
[ -3.72097526e+00, -5.78713679e-01, -2.10587338e+00],
[ -3.97001451e+00, -5.57184204e-01, -1.69690833e+00],
[ -3.98873446e+00, -2.91098598e-01, -2.30357374e+00],
[ -1.88313803e+00, -5.82916210e-01, -3.53492835e+00],
[ -3.88993013e+00, -4.54866648e-01, -1.79057867e+00],
[ -8.44327079e+00, -1.57039605e+00, 4.17907680e+00],
[ -6.87415927e+00, -5.06784790e-01, 2.19629660e+00],
[ -7.91181719e+00, -2.60384737e-01, 2.00408716e+00],
[ -7.40256671e+00, -1.91248927e-01, 2.01562798e+00],
[ -8.03750558e+00, -7.06688154e-01, 3.03704330e+00],
[ -9.28782185e+00, 3.52295446e-01, 2.88000476e+00],
[ -5.97680452e+00, -6.31080761e-01, 2.04626347e+00],
[ -8.57107720e+00, 6.29751099e-01, 2.03778301e+00],
[ -8.27351857e+00, 7.31271747e-01, 2.44051959e+00],
[ -7.85760620e+00, -1.61790452e+00, 2.42912083e+00],
[ -5.95592826e+00, -1.15410745e+00, 4.89433032e-01],
[ -7.07759451e+00, -1.41269452e-01, 1.66597584e+00],
[ -7.13145722e+00, -6.16439694e-01, 1.52795604e+00],
[ -7.08461297e+00, -4.24601727e-01, 2.68234382e+00],
[ -7.24257708e+00, -1.35963732e+00, 3.32056082e+00],
[ -6.75858385e+00, -1.49590142e+00, 1.92099452e+00],
[ -6.94722519e+00, -3.25770071e-01, 1.27359895e+00],
[ -8.40640873e+00, -9.67495213e-01, 1.98447296e+00],
[ -1.07153033e+01, 9.30482292e-01, 4.57532751e+00],
[ -6.88395814e+00, 8.73409349e-01, 1.35267572e+00],
[ -7.45594616e+00, -1.05651852e+00, 2.05562061e+00],
[ -6.46150847e+00, -1.00428141e+00, 2.14571867e+00],
[ -9.66188710e+00, 9.10445658e-01, 3.00766487e+00],
[ -6.11132736e+00, -2.75442630e-01, 5.92801474e-01],
[ -7.18699507e+00, -1.02282209e+00, 1.73262239e+00],
[ -7.49554447e+00, -6.54601137e-02, 1.00696351e+00],
[ -5.78048234e+00, -5.34955680e-01, 3.63029987e-01],
[ -5.75593581e+00, -8.39020522e-01, 4.74026003e-01],
[ -7.81592614e+00, -4.05163168e-01, 2.76491694e+00],
[ -7.12955669e+00, 4.16282683e-01, 3.08546424e-01],
[ -8.32618938e+00, 5.77413018e-01, 1.78180120e+00],
[ -7.43894969e+00, -7.80356879e-01, 3.90599998e-01],
[ -7.91883565e+00, -5.43717011e-01, 3.02045515e+00],
[ -6.10889733e+00, 9.56671062e-02, 1.66954342e-01],
[ -7.51231555e+00, 7.59888463e-01, 1.79526067e+00],
[ -8.32107145e+00, -1.71977085e-01, 1.98484283e+00],
[ -7.28937507e+00, -1.82298858e+00, 2.78172308e+00],
[ -6.84259428e+00, -5.27517358e-01, 1.29092463e+00],
[ -5.57122053e+00, -9.38450254e-01, 3.97681350e-01],
[ -6.71761453e+00, -7.92624807e-01, 9.56679520e-01],
[ -7.56176896e+00, -1.17608275e+00, 2.55899352e+00],
[ -6.24479119e+00, -1.24302978e+00, 7.26464438e-01],
[ -6.87415927e+00, -5.06784790e-01, 2.19629660e+00],
[ -7.94987323e+00, -9.82650965e-01, 2.72056746e+00],
[ -7.59863311e+00, -1.57703746e+00, 2.75477523e+00],
[ -6.70013272e+00, -1.10850864e+00, 1.46849347e+00],
[ -6.73271047e+00, -3.60640726e-02, 1.40229052e+00],
[ -6.47440185e+00, -7.76175045e-01, 1.04338387e+00],
[ -6.77553616e+00, -1.84163023e+00, 2.20274305e+00],
[ -6.29136171e+00, -8.06816934e-01, 1.30972537e+00]])
In [7]:
#listing the parameters
m.get_params()
Out[7]:
{'C': 1.0,
'class_weight': None,
'dual': False,
'fit_intercept': True,
'intercept_scaling': 1,
'max_iter': 100,
'multi_class': 'ovr',
'n_jobs': 1,
'penalty': 'l2',
'random_state': None,
'solver': 'liblinear',
'tol': 0.0001,
'verbose': 0,
'warm_start': False}
In [10]:
#fitting the data and transforming the result
m.fit_transform(iris.data,iris.target)
C:\Users\priyu\Anaconda3\lib\site-packages\sklearn\utils\deprecation.py:70: DeprecationWarning: Function transform is deprecated; Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead.
warnings.warn(msg, category=DeprecationWarning)
Out[10]:
array([[ 3.5, 1.4, 0.2],
[ 3. , 1.4, 0.2],
[ 3.2, 1.3, 0.2],
[ 3.1, 1.5, 0.2],
[ 3.6, 1.4, 0.2],
[ 3.9, 1.7, 0.4],
[ 3.4, 1.4, 0.3],
[ 3.4, 1.5, 0.2],
[ 2.9, 1.4, 0.2],
[ 3.1, 1.5, 0.1],
[ 3.7, 1.5, 0.2],
[ 3.4, 1.6, 0.2],
[ 3. , 1.4, 0.1],
[ 3. , 1.1, 0.1],
[ 4. , 1.2, 0.2],
[ 4.4, 1.5, 0.4],
[ 3.9, 1.3, 0.4],
[ 3.5, 1.4, 0.3],
[ 3.8, 1.7, 0.3],
[ 3.8, 1.5, 0.3],
[ 3.4, 1.7, 0.2],
[ 3.7, 1.5, 0.4],
[ 3.6, 1. , 0.2],
[ 3.3, 1.7, 0.5],
[ 3.4, 1.9, 0.2],
[ 3. , 1.6, 0.2],
[ 3.4, 1.6, 0.4],
[ 3.5, 1.5, 0.2],
[ 3.4, 1.4, 0.2],
[ 3.2, 1.6, 0.2],
[ 3.1, 1.6, 0.2],
[ 3.4, 1.5, 0.4],
[ 4.1, 1.5, 0.1],
[ 4.2, 1.4, 0.2],
[ 3.1, 1.5, 0.1],
[ 3.2, 1.2, 0.2],
[ 3.5, 1.3, 0.2],
[ 3.1, 1.5, 0.1],
[ 3. , 1.3, 0.2],
[ 3.4, 1.5, 0.2],
[ 3.5, 1.3, 0.3],
[ 2.3, 1.3, 0.3],
[ 3.2, 1.3, 0.2],
[ 3.5, 1.6, 0.6],
[ 3.8, 1.9, 0.4],
[ 3. , 1.4, 0.3],
[ 3.8, 1.6, 0.2],
[ 3.2, 1.4, 0.2],
[ 3.7, 1.5, 0.2],
[ 3.3, 1.4, 0.2],
[ 3.2, 4.7, 1.4],
[ 3.2, 4.5, 1.5],
[ 3.1, 4.9, 1.5],
[ 2.3, 4. , 1.3],
[ 2.8, 4.6, 1.5],
[ 2.8, 4.5, 1.3],
[ 3.3, 4.7, 1.6],
[ 2.4, 3.3, 1. ],
[ 2.9, 4.6, 1.3],
[ 2.7, 3.9, 1.4],
[ 2. , 3.5, 1. ],
[ 3. , 4.2, 1.5],
[ 2.2, 4. , 1. ],
[ 2.9, 4.7, 1.4],
[ 2.9, 3.6, 1.3],
[ 3.1, 4.4, 1.4],
[ 3. , 4.5, 1.5],
[ 2.7, 4.1, 1. ],
[ 2.2, 4.5, 1.5],
[ 2.5, 3.9, 1.1],
[ 3.2, 4.8, 1.8],
[ 2.8, 4. , 1.3],
[ 2.5, 4.9, 1.5],
[ 2.8, 4.7, 1.2],
[ 2.9, 4.3, 1.3],
[ 3. , 4.4, 1.4],
[ 2.8, 4.8, 1.4],
[ 3. , 5. , 1.7],
[ 2.9, 4.5, 1.5],
[ 2.6, 3.5, 1. ],
[ 2.4, 3.8, 1.1],
[ 2.4, 3.7, 1. ],
[ 2.7, 3.9, 1.2],
[ 2.7, 5.1, 1.6],
[ 3. , 4.5, 1.5],
[ 3.4, 4.5, 1.6],
[ 3.1, 4.7, 1.5],
[ 2.3, 4.4, 1.3],
[ 3. , 4.1, 1.3],
[ 2.5, 4. , 1.3],
[ 2.6, 4.4, 1.2],
[ 3. , 4.6, 1.4],
[ 2.6, 4. , 1.2],
[ 2.3, 3.3, 1. ],
[ 2.7, 4.2, 1.3],
[ 3. , 4.2, 1.2],
[ 2.9, 4.2, 1.3],
[ 2.9, 4.3, 1.3],
[ 2.5, 3. , 1.1],
[ 2.8, 4.1, 1.3],
[ 3.3, 6. , 2.5],
[ 2.7, 5.1, 1.9],
[ 3. , 5.9, 2.1],
[ 2.9, 5.6, 1.8],
[ 3. , 5.8, 2.2],
[ 3. , 6.6, 2.1],
[ 2.5, 4.5, 1.7],
[ 2.9, 6.3, 1.8],
[ 2.5, 5.8, 1.8],
[ 3.6, 6.1, 2.5],
[ 3.2, 5.1, 2. ],
[ 2.7, 5.3, 1.9],
[ 3. , 5.5, 2.1],
[ 2.5, 5. , 2. ],
[ 2.8, 5.1, 2.4],
[ 3.2, 5.3, 2.3],
[ 3. , 5.5, 1.8],
[ 3.8, 6.7, 2.2],
[ 2.6, 6.9, 2.3],
[ 2.2, 5. , 1.5],
[ 3.2, 5.7, 2.3],
[ 2.8, 4.9, 2. ],
[ 2.8, 6.7, 2. ],
[ 2.7, 4.9, 1.8],
[ 3.3, 5.7, 2.1],
[ 3.2, 6. , 1.8],
[ 2.8, 4.8, 1.8],
[ 3. , 4.9, 1.8],
[ 2.8, 5.6, 2.1],
[ 3. , 5.8, 1.6],
[ 2.8, 6.1, 1.9],
[ 3.8, 6.4, 2. ],
[ 2.8, 5.6, 2.2],
[ 2.8, 5.1, 1.5],
[ 2.6, 5.6, 1.4],
[ 3. , 6.1, 2.3],
[ 3.4, 5.6, 2.4],
[ 3.1, 5.5, 1.8],
[ 3. , 4.8, 1.8],
[ 3.1, 5.4, 2.1],
[ 3.1, 5.6, 2.4],
[ 3.1, 5.1, 2.3],
[ 2.7, 5.1, 1.9],
[ 3.2, 5.9, 2.3],
[ 3.3, 5.7, 2.5],
[ 3. , 5.2, 2.3],
[ 2.5, 5. , 1.9],
[ 3. , 5.2, 2. ],
[ 3.4, 5.4, 2.3],
[ 3. , 5.1, 1.8]])
In [11]:
#predict - in the output below, 0-Setosa, 1-Versicolour, 2-Virginica
m.predict(iris.data)
Out[11]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
In [12]:
#predict the probability estimates
m.predict_proba(iris.data)
Out[12]:
array([[ 8.79681649e-01, 1.20307538e-01, 1.08131372e-05],
[ 7.99706325e-01, 2.00263292e-01, 3.03825365e-05],
[ 8.53796795e-01, 1.46177302e-01, 2.59031285e-05],
[ 8.25383127e-01, 1.74558937e-01, 5.79356669e-05],
[ 8.97323628e-01, 1.02665167e-01, 1.12050036e-05],
[ 9.26986574e-01, 7.30004562e-02, 1.29693872e-05],
[ 8.95064974e-01, 1.04895775e-01, 3.92506205e-05],
[ 8.61839691e-01, 1.38141399e-01, 1.89095833e-05],
[ 8.03156719e-01, 1.96758495e-01, 8.47861140e-05],
[ 7.95421554e-01, 2.04552763e-01, 2.56832240e-05],
[ 8.92083069e-01, 1.07910759e-01, 6.17176870e-06],
[ 8.63364991e-01, 1.36600589e-01, 3.44201798e-05],
[ 7.88177618e-01, 2.11794929e-01, 2.74526810e-05],
[ 8.35079702e-01, 1.64888155e-01, 3.21426418e-05],
[ 9.28349898e-01, 7.16491356e-02, 9.66254924e-07],
[ 9.64535656e-01, 3.54620850e-02, 2.25877936e-06],
[ 9.40906153e-01, 5.90890027e-02, 4.84421830e-06],
[ 8.91740161e-01, 1.08245661e-01, 1.41772124e-05],
[ 8.96525617e-01, 1.03467608e-01, 6.77567332e-06],
[ 9.23615524e-01, 7.63726510e-02, 1.18248373e-05],
[ 8.30668332e-01, 1.69316458e-01, 1.52093733e-05],
[ 9.21914602e-01, 7.80675598e-02, 1.78384021e-05],
[ 9.26584671e-01, 7.34068679e-02, 8.46162713e-06],
[ 8.67785629e-01, 1.32146178e-01, 6.81931916e-05],
[ 8.41271506e-01, 1.58655904e-01, 7.25903122e-05],
[ 7.77263282e-01, 2.22695181e-01, 4.15365716e-05],
[ 8.81389224e-01, 1.18568969e-01, 4.18075826e-05],
[ 8.69974782e-01, 1.30013638e-01, 1.15794893e-05],
[ 8.60034106e-01, 1.39955486e-01, 1.04082979e-05],
[ 8.32052869e-01, 1.67892968e-01, 5.41625519e-05],
[ 8.07811588e-01, 1.92136477e-01, 5.19350231e-05],
[ 8.72544939e-01, 1.27438925e-01, 1.61360155e-05],
[ 9.33948477e-01, 6.60477336e-02, 3.78900866e-06],
[ 9.46250501e-01, 5.37475145e-02, 1.98493064e-06],
[ 7.95421554e-01, 2.04552763e-01, 2.56832240e-05],
[ 8.47610513e-01, 1.52377535e-01, 1.19520539e-05],
[ 8.70019435e-01, 1.29976367e-01, 4.19728170e-06],
[ 7.95421554e-01, 2.04552763e-01, 2.56832240e-05],
[ 8.31024910e-01, 1.68917216e-01, 5.78737851e-05],
[ 8.57737250e-01, 1.42246900e-01, 1.58501104e-05],
[ 9.00222082e-01, 9.97646975e-02, 1.32206853e-05],
[ 6.90741687e-01, 3.09094698e-01, 1.63615590e-04],
[ 8.66068303e-01, 1.33887708e-01, 4.39884356e-05],
[ 9.16308833e-01, 8.36288777e-02, 6.22895883e-05],
[ 9.15519114e-01, 8.44392129e-02, 4.16734713e-05],
[ 8.20309627e-01, 1.79642381e-01, 4.79919885e-05],
[ 9.09855663e-01, 9.01327650e-02, 1.15724381e-05],
[ 8.51214451e-01, 1.48746052e-01, 3.94971199e-05],
[ 8.95519736e-01, 1.04472911e-01, 7.35323849e-06],
[ 8.51563342e-01, 1.48419676e-01, 1.69821772e-05],
[ 2.98900777e-02, 8.60393138e-01, 1.09716785e-01],
[ 3.74487166e-02, 7.05572459e-01, 2.56978825e-01],
[ 1.17957675e-02, 7.48252356e-01, 2.39951876e-01],
[ 1.32920493e-02, 6.51770445e-01, 3.34937506e-01],
[ 1.09868088e-02, 6.98832091e-01, 2.90181101e-01],
[ 1.07669519e-02, 5.83013186e-01, 4.06219862e-01],
[ 2.15200540e-02, 5.37732882e-01, 4.40747064e-01],
[ 1.08418544e-01, 7.68766189e-01, 1.22815267e-01],
[ 1.77270021e-02, 8.27562690e-01, 1.54710308e-01],
[ 3.30493839e-02, 5.28708770e-01, 4.38241846e-01],
[ 2.93117962e-02, 7.72717609e-01, 1.97970595e-01],
[ 4.09569813e-02, 6.19765980e-01, 3.39277039e-01],
[ 1.95378252e-02, 8.79697992e-01, 1.00764183e-01],
[ 8.73285529e-03, 5.96503817e-01, 3.94763328e-01],
[ 1.67434866e-01, 7.12756209e-01, 1.19808925e-01],
[ 4.75535678e-02, 8.43626581e-01, 1.08819852e-01],
[ 1.22530319e-02, 4.23869480e-01, 5.63877488e-01],
[ 3.84753639e-02, 8.50175432e-01, 1.11349204e-01],
[ 3.09968794e-03, 5.96264678e-01, 4.00635634e-01],
[ 3.59781700e-02, 8.08752206e-01, 1.55269624e-01],
[ 6.20745751e-03, 2.73106189e-01, 7.20686354e-01],
[ 5.81151228e-02, 8.19701311e-01, 1.22183566e-01],
[ 1.95840574e-03, 5.33800891e-01, 4.64240703e-01],
[ 8.77628703e-03, 7.04654010e-01, 2.86569703e-01],
[ 3.69274341e-02, 8.38990091e-01, 1.24082475e-01],
[ 3.61807169e-02, 8.28744840e-01, 1.35074443e-01],
[ 8.14489700e-03, 7.77156946e-01, 2.14698157e-01],
[ 4.64006697e-03, 5.23164549e-01, 4.72195384e-01],
[ 1.33500103e-02, 5.63205976e-01, 4.23444014e-01],
[ 1.28473017e-01, 8.31361691e-01, 4.01652917e-02],
[ 3.60902230e-02, 8.03217466e-01, 1.60692311e-01],
[ 5.05096042e-02, 8.46149445e-01, 1.03340951e-01],
[ 5.69724571e-02, 8.11250984e-01, 1.31776559e-01],
[ 1.22453086e-03, 3.99201919e-01, 5.99573550e-01],
[ 1.03123407e-02, 3.65034695e-01, 6.24652965e-01],
[ 4.17476538e-02, 4.77844283e-01, 4.80408063e-01],
[ 1.90525287e-02, 7.45629538e-01, 2.35317933e-01],
[ 7.05352060e-03, 7.56932682e-01, 2.36013798e-01],
[ 5.57541864e-02, 6.67410837e-01, 2.76834977e-01],
[ 2.10790319e-02, 6.62362244e-01, 3.16558724e-01],
[ 8.98003281e-03, 5.99716389e-01, 3.91303578e-01],
[ 1.52196906e-02, 6.32329159e-01, 3.52451150e-01],
[ 3.47695685e-02, 7.98625645e-01, 1.66604786e-01],
[ 9.15416570e-02, 7.95877151e-01, 1.12581192e-01],
[ 1.98418694e-02, 6.40871800e-01, 3.39286330e-01],
[ 4.81040905e-02, 7.31039981e-01, 2.20855929e-01],
[ 3.44565240e-02, 6.77463657e-01, 2.88079819e-01],
[ 3.38822929e-02, 7.96899915e-01, 1.69217792e-01],
[ 2.54574647e-01, 6.90791330e-01, 5.46340233e-02],
[ 3.63488963e-02, 7.04234211e-01, 2.59416893e-01],
[ 1.86036022e-04, 1.48760823e-01, 8.51053141e-01],
[ 8.09069371e-04, 2.94422745e-01, 7.04768186e-01],
[ 2.78126551e-04, 3.30535386e-01, 6.69186488e-01],
[ 4.56288643e-04, 3.38732197e-01, 6.60811514e-01],
[ 2.51393977e-04, 2.57092194e-01, 7.42656412e-01],
[ 6.03186905e-05, 3.82744333e-01, 6.17195349e-01],
[ 2.04838186e-03, 2.81103453e-01, 7.16848165e-01],
[ 1.23247784e-04, 4.24393655e-01, 5.75483097e-01],
[ 1.59929758e-04, 4.23195996e-01, 5.76644074e-01],
[ 3.56390886e-04, 1.52542892e-01, 8.47100717e-01],
[ 2.99635433e-03, 2.78024684e-01, 7.18978962e-01],
[ 6.45242833e-04, 3.55681241e-01, 6.43673516e-01],
[ 6.81029987e-04, 2.98859721e-01, 7.00459249e-01],
[ 6.28418142e-04, 2.96807692e-01, 7.02563890e-01],
[ 6.10997845e-04, 1.74593604e-01, 8.24795398e-01],
[ 1.09757190e-03, 1.73257823e-01, 8.25644605e-01],
[ 7.99254871e-04, 3.48929847e-01, 6.50270898e-01],
[ 1.93443479e-04, 2.38473708e-01, 7.61332849e-01],
[ 1.30064976e-05, 4.20137191e-01, 5.79849802e-01],
[ 6.81548718e-04, 4.69975854e-01, 5.29342597e-01],
[ 5.04477452e-04, 2.25292722e-01, 7.74202801e-01],
[ 1.33913767e-03, 2.30143290e-01, 7.68517573e-01],
[ 3.82097113e-05, 4.28006955e-01, 5.71954836e-01],
[ 2.05299242e-03, 4.00421888e-01, 5.97525119e-01],
[ 6.77847072e-04, 2.37204010e-01, 7.62118143e-01],
[ 4.56383243e-04, 3.97527741e-01, 6.02015876e-01],
[ 3.19858866e-03, 3.83866887e-01, 6.12934525e-01],
[ 3.42364119e-03, 3.27541103e-01, 6.69035256e-01],
[ 3.00544917e-04, 2.98288662e-01, 7.01410793e-01],
[ 6.78376797e-04, 5.10705151e-01, 4.88616472e-01],
[ 1.61719140e-04, 4.27941843e-01, 5.71896438e-01],
[ 6.44775841e-04, 3.44845359e-01, 6.54509865e-01],
[ 2.75279882e-04, 2.78027400e-01, 7.21697320e-01],
[ 2.07731418e-03, 4.90652652e-01, 5.07270034e-01],
[ 3.54683506e-04, 4.42580814e-01, 5.57064503e-01],
[ 1.82017584e-04, 3.42008155e-01, 6.57809828e-01],
[ 6.30908753e-04, 1.28602511e-01, 8.70766580e-01],
[ 9.21940559e-04, 3.20888055e-01, 6.78190005e-01],
[ 4.29311663e-03, 3.18426266e-01, 6.77280618e-01],
[ 1.16680587e-03, 3.00989509e-01, 6.97843685e-01],
[ 4.46290865e-04, 2.02461924e-01, 7.97091785e-01],
[ 2.15227432e-03, 2.48822456e-01, 7.49025270e-01],
[ 8.09069371e-04, 2.94422745e-01, 7.04768186e-01],
[ 2.91162367e-04, 2.24919706e-01, 7.74789132e-01],
[ 4.50477099e-04, 1.53984748e-01, 8.45564775e-01],
[ 1.15724730e-03, 2.33616548e-01, 7.65226205e-01],
[ 9.19025197e-04, 3.79220387e-01, 6.19860588e-01],
[ 1.45811816e-03, 2.98379693e-01, 7.00162189e-01],
[ 1.09779827e-03, 1.31785617e-01, 8.67116585e-01],
[ 1.68397530e-03, 2.81057800e-01, 7.17258224e-01]])
In [13]:
#predict the log of probability estimates
m.predict_log_proba(iris.data)
Out[13]:
array([[ -0.1281952 , -2.117704 , -11.43474875],
[ -0.22351071, -1.60812232, -10.40164257],
[ -0.15806206, -1.922935 , -10.56114681],
[ -0.1919076 , -1.74549284, -9.75617735],
[ -0.10833869, -2.27628239, -11.39915013],
[ -0.0758162 , -2.61728959, -11.25291881],
[ -0.11085897, -2.25478804, -10.1455433 ],
[ -0.148686 , -1.97947749, -10.87584171],
[ -0.21920542, -1.62577821, -9.37537878],
[ -0.22888305, -1.58692933, -10.56967254],
[ -0.11419602, -2.2264507 , -11.9955251 ],
[ -0.14691774, -1.99069402, -10.27686754],
[ -0.23803181, -1.55213679, -10.50304673],
[ -0.18022811, -1.80248788, -10.34532701],
[ -0.07434657, -2.63597419, -13.84983814],
[ -0.03610848, -3.33929118, -13.00068599],
[ -0.06091188, -2.82871045, -12.23772467],
[ -0.11458049, -2.22335199, -11.16387464],
[ -0.10922841, -2.26849668, -11.90217181],
[ -0.07945939, -2.57213062, -11.34530838],
[ -0.18552468, -1.77598578, -11.09359866],
[ -0.08130268, -2.55018068, -10.934157 ],
[ -0.07624985, -2.61173778, -11.67996907],
[ -0.14181057, -2.02384656, -9.59316583],
[ -0.17284083, -1.84101755, -9.53067909],
[ -0.25197614, -1.50195134, -10.08893628],
[ -0.12625595, -2.13226047, -10.08243283],
[ -0.13929105, -2.04011593, -11.36627519],
[ -0.15078323, -1.96643087, -11.47290719],
[ -0.1838593 , -1.7844286 , -9.82352081],
[ -0.21342643, -1.64954934, -9.86551718],
[ -0.13634112, -2.06011805, -11.03445679],
[ -0.06833401, -2.71737756, -12.48340614],
[ -0.05524795, -2.92345785, -13.12992659],
[ -0.22888305, -1.58692933, -10.56967254],
[ -0.16533405, -1.88139406, -11.33460742],
[ -0.13923973, -2.04040263, -12.38107346],
[ -0.22888305, -1.58692933, -10.56967254],
[ -0.18509551, -1.77834653, -9.75724604],
[ -0.15345746, -1.950191 , -11.05233409],
[ -0.10511379, -2.30494089, -11.23372789],
[ -0.36998935, -1.17410758, -8.71799084],
[ -0.1437915 , -2.01075383, -10.03158379],
[ -0.08740182, -2.48136639, -9.68371627],
[ -0.08826404, -2.47172338, -10.08564581],
[ -0.19807342, -1.71678718, -9.94447647],
[ -0.0944693 , -2.40647153, -11.36688431],
[ -0.16109118, -1.90551478, -10.1392828 ],
[ -0.11035102, -2.25882747, -11.82036973],
[ -0.16068139, -1.90771137, -10.98334616],
[ -3.5102287 , -0.15036586, -2.20985292],
[ -3.28478284, -0.34874581, -1.35876159],
[ -4.4400145 , -0.29001498, -1.42731689],
[ -4.32058922, -0.42806286, -1.09381131],
[ -4.51105993, -0.35834478, -1.23725007],
[ -4.53127384, -0.53954548, -0.90086073],
[ -3.83877003, -0.62039334, -0.81928412],
[ -2.22175614, -0.2629684 , -2.09707395],
[ -4.03266626, -0.18927042, -1.86620089],
[ -3.40975235, -0.63731753, -0.82498436],
[ -3.52976524, -0.25784162, -1.61963677],
[ -3.195233 , -0.47841332, -1.08093828],
[ -3.93540294, -0.12817662, -2.29497231],
[ -4.7406629 , -0.51666964, -0.92946886],
[ -1.78716086, -0.33861584, -2.1218571 ],
[ -3.04589846, -0.17004532, -2.2180615 ],
[ -4.40198187, -0.8583297 , -0.57291827],
[ -3.25773714, -0.16231256, -2.19508404],
[ -5.77645384, -0.51707062, -0.91470291],
[ -3.32484291, -0.21226271, -1.86259216],
[ -5.08200388, -1.29789459, -0.32755125],
[ -2.84532936, -0.19881526, -2.10223072],
[ -6.23562454, -0.62773237, -0.7673521 ],
[ -4.73570185, -0.35004836, -1.24977348],
[ -3.29880053, -0.17555638, -2.08680881],
[ -3.31922899, -0.18784296, -2.00192922],
[ -4.81036368, -0.25211296, -1.53852216],
[ -5.37302648, -0.64785924, -0.75036243],
[ -4.31623812, -0.57410986, -0.85933397],
[ -2.05203638, -0.18469033, -3.21475205],
[ -3.32173328, -0.21912978, -1.82826386],
[ -2.98559178, -0.16705929, -2.26972155],
[ -2.86518734, -0.2091778 , -2.02664753],
[ -6.70519748, -0.91828793, -0.51153663],
[ -4.57441397, -1.00776288, -0.47055904],
[ -3.17611203, -0.73847037, -0.7331194 ],
[ -3.96055544, -0.2935264 , -1.44681777],
[ -4.95422841, -0.27848096, -1.44386501],
[ -2.88680278, -0.40434948, -1.2843337 ],
[ -3.85947648, -0.41194268, -1.15024651],
[ -4.71275174, -0.51129842, -0.93827161],
[ -4.18516525, -0.4583452 , -1.04284325],
[ -3.35901274, -0.22486297, -1.79213082],
[ -2.39096114, -0.22831044, -2.18408061],
[ -3.91996096, -0.44492584, -1.0809109 ],
[ -3.03438806, -0.31328713, -1.5102447 ],
[ -3.36805692, -0.38939937, -1.24451769],
[ -3.38486273, -0.22702619, -1.77656868],
[ -1.36816118, -0.36991748, -2.90709845],
[ -3.31459144, -0.35064429, -1.34931889],
[ -8.58957024, -1.90541548, -0.16128071],
[ -7.1196259 , -1.22273864, -0.34988634],
[ -8.18743433, -1.10704156, -0.4016925 ],
[ -7.69238496, -1.08254546, -0.41428663],
[ -8.28848922, -1.35832053, -0.29752177],
[ -9.71586854, -0.96038805, -0.48256969],
[ -6.19070513, -1.26903252, -0.33289123],
[ -9.00131372, -0.85709382, -0.55254542],
[ -8.74077585, -0.85991986, -0.55053006],
[ -7.93948244, -1.88030946, -0.16593568],
[ -5.81035895, -1.28004538, -0.32992318],
[ -7.34588383, -1.03372034, -0.44056364],
[ -7.29190422, -1.20778098, -0.35601909],
[ -7.37230478, -1.21467085, -0.35301894],
[ -7.40041713, -1.74529427, -0.19261993],
[ -6.8146549 , -1.75297449, -0.19159086],
[ -7.13183068, -1.05288439, -0.43036624],
[ -8.55052519, -1.43349622, -0.27268463],
[-11.25006151, -0.86717397, -0.54498617],
[ -7.29114282, -0.75507396, -0.63611943],
[ -7.59198741, -1.49035474, -0.25592142],
[ -6.6157294 , -1.46905317, -0.26329185],
[-10.17242085, -0.84861583, -0.55869525],
[ -6.18845683, -0.91523657, -0.51495896],
[ -7.29658885, -1.43883471, -0.27165369],
[ -7.69217766, -0.92249056, -0.50747146],
[ -5.74504561, -0.95745944, -0.48949716],
[ -5.67705062, -1.11614173, -0.40191852],
[ -8.10991334, -1.2096936 , -0.35466155],
[ -7.29580768, -0.67196286, -0.71617741],
[ -8.72964943, -0.84876797, -0.55879736],
[ -7.34660784, -1.0646592 , -0.42386862],
[ -8.19772222, -1.28003561, -0.32614945],
[ -6.17667948, -0.71201883, -0.67871181],
[ -7.9442847 , -0.8151322 , -0.58507424],
[ -8.61140726, -1.0729207 , -0.4188394 ],
[ -7.36834931, -2.05102894, -0.13838133],
[ -6.98902981, -1.13666296, -0.38832779],
[ -5.45074232, -1.14436434, -0.38966959],
[ -6.75348529, -1.20067987, -0.35976015],
[ -7.71453965, -1.59720344, -0.22678544],
[ -6.14123017, -1.39101567, -0.28898256],
[ -7.1196259 , -1.22273864, -0.34988634],
[ -8.14162948, -1.4920118 , -0.25516437],
[ -7.70520332, -1.87090172, -0.1677505 ],
[ -6.76171111, -1.45407419, -0.2675838 ],
[ -6.99219702, -0.96963775, -0.47826068],
[ -6.5306086 , -1.20938847, -0.35644327],
[ -6.81444867, -2.02657879, -0.14258184],
[ -6.38659803, -1.26919494, -0.33231936]])
In [14]:
#returns the mean accuracy on the given test data and labels
m.score(iris.data,iris.target)
Out[14]:
0.95999999999999996
In [16]:
#convert coefficient matrix to sparse format
m.sparsify()
Out[16]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
Content source: harishkrao/Machine-Learning
Similar notebooks: