title: Converting LinearSVC's decision function to probabilities (Scikit learn python )
body: <p>I use linear SVM from scikit learn (LinearSVC) for binary classification problem. I understand that LinearSVC can give me the predicted labels, and the decision scores but I wanted probability estimates (confidence in the label). I want to continue using LinearSVC because of speed (as compared to sklearn.svm.SVC with linear kernel) Is it reasonable to use a logistic function to convert the decision scores to probabilities? </p>
  
  <pre><code>import sklearn.svm as suppmach
  # Fit model:
  svmmodel=suppmach.LinearSVC(penalty='l1',C=1)
  predicted_test= svmmodel.predict(x_test)
  predicted_test_scores= svmmodel.decision_function(x_test) 
  </code></pre>
  
  <p>I want to check if it makes sense to obtain Probability estimates simply as [1 / (1 + exp(-x)) ]  where x is the decision score. </p>
  
  <p>Alternately, are there other options wrt classifiers that I can use to do this efficiently? </p>
  
  <p>Thanks. </p>