Evaluating Feature Selection Algorithms for Different Machine Learning Models
Different models respond uniquely to filter, wrapper, embedded, and advanced methods (e.g., SHAP, Boruta, Autoencoders), making it essential to test multiple approaches. Feature selection methods should be chosen considering the dataset size, the nature of the model, and system resource availability.
