Converting LinearSVC’s decision function to probabilities (Scikit learn python )

scikit-learn provides CalibratedClassifierCV which can be used to solve this problem: it allows to add probability output to LinearSVC or any other classifier which implements decision_function method:

svm = LinearSVC()
clf = CalibratedClassifierCV(svm) 
clf.fit(X_train, y_train)
y_proba = clf.predict_proba(X_test)

User guide has a nice section on that. By default CalibratedClassifierCV+LinearSVC will get you Platt scaling, but it also provides other options (isotonic regression method), and it is not limited to SVM classifiers.