I have a large dataset (195 features x 20m samples) that I have trained using XGBoost. I would like to see if a genetic algorithm can beat XGBoost since the data has so much noise it is prone to overfitting.
I would like to use a tree-based model so I don't have to standardize the data, and the features do have some interrelationships.
Are there any python packages that have this all done? Ie.that can create trees through a genetic optimization process?