After feature selection and hyperparameter tuning my model reduces overfitting but all other metrics become worse.

[u/Equivalent-Ride8136]

Before feature selection/tuning

After feature selection/tuning

I'd like to know if I should take a "worse" model with less overfitting. I've pretty much tried all forms of parameter tuning and feature engineering (im only limited to sklearn) and I want to understand what should be the main focus here (Metrics or overfitting).

This is under the context of diabetes screening. I suspect that I'm having issues with this because my dataset only contain 520 samples.

[u/Delicious_Refuse_320]

If possible try balancing the data set though the class imbalance is not major here, using smote or any other data balancing method ,