“…While computationally efficient, filter methods may overlook feature interactions. 16 , 17 , 18 , 19 Wrapper methods, on the other hand, evaluate feature subsets by training and evaluating models with different feature combinations, employing search strategies like forward selection or backward elimination to find the optimal subset. While effective, wrapper methods can be computationally expensive due to repeated model training.…”