predict_proba for a cross-validated model
This is now implemented as part of scikit-learn version 0.18. You can pass a 'method' string parameter to the cross_val_predict method. Documentation is here.
Example:
proba = cross_val_predict(logreg, X, y, cv=cv, method='predict_proba')Also note that this is part of the new sklearn.model_selection package so you will need this import:
from sklearn.model_selection import cross_val_predict
An easy workaround for this is to create a wrapper class, which for your case would be
class proba_logreg(LogisticRegression): def predict(self, X): return LogisticRegression.predict_proba(self, X)and then pass an instance of it as the classifier object to cross_val_predict
# cross validation probabilitiesprobas = cross_val_predict(proba_logreg(), X, y, cv=cv)
There is a function cross_val_predict that gives you the predicted values, but there is no such function for "predict_proba" yet. Maybe we could make that an option.