How do you do feature selection and know which features actually help?

Feature selection aims to reduce noise and improve generalization. Approaches: - Filter methods (correlation, mutual information) - Wrapper methods (RFE) - Embedded methods (L1 regularization, tree importance) Always validate with cross-validation, watch leakage, and prefer simpler models when performance is similar for better explainability and maintenance.