Answer
Use correlation, recursive feature elimination,
or model-based selectors.
Improves performance and reduces dimensionality.
or model-based selectors.
Improves performance and reduces dimensionality.
Quick Answer
Feature selection reduces noise and overfitting, speeds training. Methods: filter (correlation, chi-squared, mutual information - no model needed), wrapper (RFE - recursive feature elimination, trains model), embedded (L1 regularization - Lasso sets irrelevant feature weights to 0). SelectKBest, RFECV in scikit-learn. Feature importance from tree models. Start with filter methods for speed.
S
SugharaIQ Editorial Team
Verified Answer
This answer has been peer-reviewed by industry experts holding senior engineering roles to ensure technical accuracy and relevance for modern interview standards.
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Source: SugharaIQ