Feature Selection Methods
Filter (variance, correlation), wrapper (RFE), embedded (L1 regularization) methods.
Why Select Features
Feature selection keeps only the most useful columns. Fewer features mean faster training, less overfitting, and easier interpretation. Removing noise often improves accuracy too.
Three Families of Methods
Selection methods fall into three groups:
- Filter rank features by a statistic (fast, model-free)
- Wrapper search subsets by training models (slow, accurate)
- Embedded selection happens during model fitting