When I use the hyperopt library to tune my Random Forest classifier, I get the following results:
Hyperopt estimated optimum {'max_depth': 10.0, 'n_estimators': 300.0}
However, when I train the model using its default hyperparameters, all of the evaluation metrics (Precision, Recall, F1, iba, AUC) return higher values compared to the tuned model. Should I still follow the tuned parameters? Or ignore the results of the tuning process, as it is not helping to improve the results?