When using the python / sklearn API of xgboost are the probabilities obtained via the predict_proba
method "real probabilities" or do I have to use logit:raw
and manually calculate the sigmoid function?
I wanted to experiment with different cutoff points. Currently using binary:lgistic
via the sklearn:XGBClassifier
the probabilities returned from the prob_a
method rather resemble 2 classes and not a continuous function where changing the cut-off point impacts the final scoring.
Is this the right way to obtain probabilities for experimenting with the cutoff value?