You're training a supervised learning model and have many candidate features, some redundant, some noisy, and some only weakly useful.
What is your approach to feature selection in machine learning?
How you structure feature selection rather than naming one methodWhether you distinguish filter, wrapper, and embedded approachesHow you avoid leakage during selectionHow you validate that a smaller feature set still generalizes