I use linear SVM from scikit learn (LinearSVC) for binary classification problems. 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 the linear kernel) Is it reasonable to use a logistic function to convert the decision scores to probabilities?
import sklearn.svm as suppmach
# Fit model:
I want to check if it makes sense to obtain Probability estimates simply as [1 / (1 + exp(-x)) ] where x is the decision score.
Alternately, are there other options wrt classifiers that I can use to do this efficiently?