I am getting a strange output from sklearn's LogisticRegression, where my trained model classifies all observations as 1s.
In : logit = LogisticRegression(C=10e9, random_state=42) model = logit.fit(X_train, y_train) classes = model.predict(X_test) probs = model.predict_proba(X_test) print np.bincount(classes) Out : [ 0 2458]
How is this possible?
I know that there is another post on this (here), but it does not answer this question. I understand that my classes are not balanced (this uniform classification goes away when I enter the argument class_weights = balanced).
However, I want to understand why sklearn is classifying predicted probabilities of less than 0.5 as a positive event.