Answer / Chandan Kumar Keshri

Feature selection techniques are used to identify the most important variables in a dataset. They help to reduce dimensionality, improve model performance, and prevent overfitting. Common feature selection techniques include filter methods (e.g., correlation-based feature selection, mutual information), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., LASSO regression).

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