SKLearn how to get decision probabilities for LinearSVC classifier

Question

I am using scikit-learn\'s linearSVC classifier for text mining. I have the y value as a label 0/1 and the X value as the TfidfVectorizer of the text document.

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Answer 1

You can't. However you can use sklearn.svm.SVC with kernel='linear' and probability=True

It may run longer, but you can get probabilities from this classifier by using predict_proba method.

clf=sklearn.svm.SVC(kernel='linear',probability=True)
clf.fit(X,y)
clf.predict_proba(X_test)

Answer 2

If you insist on using the LinearSVC class, you can wrap it in a sklearn.calibration.CalibratedClassifierCV object and fit the calibrated classifier which will give you a probabilistic classifier.

from sklearn.svm import LinearSVC
from sklearn.calibration import CalibratedClassifierCV
from sklearn import datasets

#Load iris dataset
iris = datasets.load_iris()
X = iris.data[:, :2] # Using only two features
y = iris.target      #3 classes: 0, 1, 2

linear_svc = LinearSVC()     #The base estimator

# This is the calibrated classifier which can give probabilistic classifier
calibrated_svc = CalibratedClassifierCV(linear_svc,
                                        method='sigmoid',  #sigmoid will use Platt's scaling. Refer to documentation for other methods.
                                        cv=3) 
calibrated_svc.fit(X, y)


# predict
prediction_data = [[2.3, 5],
                   [4, 7]]
predicted_probs = calibrated_svc.predict_proba(prediction_data)  #important to use predict_proba
print predicted_probs

Here is the output:

[[  9.98626760e-01   1.27594869e-03   9.72912751e-05]
 [  9.99578199e-01   1.79053170e-05   4.03895759e-04]]

which shows probabilities for each class for each data point.