I'm using XGBoost for a binary classification problem. There is no negative label, only 1 and 0.
I tunned the hyperparameters using Bayesian optimization then tried to train the final model with the optimized hyperparameters.
Mdl_XGB = xgb.train(OptimizedParams, dtrain)
scores_train = Mdl_XGB.predict(dtrain)
scores_test = Mdl_XGB.predict(dtest)
My problem is that the predicted scores for both train and test sets include both negative values and numbers greater than one. The scores are between -0.23 and 1.13.
Shouldn't these scores present the probability of belonging to class 1 (positive class)?
pred_train = Mdl_XGB.predict(dtrain). $\endgroup$