Overview
Feature selection aims to improve model performance, reduce overfitting, and increase interpretability by removing irrelevant or redundant data. It is a critical step in the machine learning pipeline.
Techniques
- Filter Methods: Using statistical measures (e.g., correlation, chi-square) to rank features independently of the model.
- Wrapper Methods: Using a predictive model to evaluate combinations of features (e.g., Recursive Feature Elimination).
- Embedded Methods: Feature selection is performed as part of the model training process (e.g., LASSO regression).