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:rawand manually calculate the sigmoid function?I wanted to experiment with different cutoff points. Currently using binary:lgisticvia 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?
Xgboost predict probabilities
predictive modeling;scikit learn;xgboost;probability
LightGBM forum was the answer ;) https://github.com/Microsoft/LightGBM/issues/272#issuecomment-276168493apparently, my model is fitting the result very good, i.e. it is very sure about the class probabilitiesI did not expect such clear-cut boundaries and thus was confused if this could be correct.