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AI & Machine Learning · Intermediate

Feature Selection

Choosing a subset of the most relevant features and discarding redundant or irrelevant ones.

Definition

Feature selection reduces overfitting, training time and model complexity while improving interpretability. Methods include filter approaches (correlation, mutual information), wrapper approaches (recursive feature elimination) and embedded approaches (L1 regularization, tree importance). It keeps original features rather than creating new ones.

Exam tip

L1 (Lasso) regularization performs implicit feature selection by driving weights to zero.

Related terms