Xgboost predict probabilities 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?
Curious Georg if you ran across this article in your pursuit of trying to generate probabilities. It is worth noting that binary:logistic and multi:softprob return predicted probability of each data point belonging to each class. 

You can look here to see how the following code is used: