Feature Selection

Feature selection evaluates available variables and determines which ones contribute most effectively to a machine learning model’s predictions. Techniques may include statistical tests, correlation analysis, feature importance scores, and iterative selection methods. Unlike dimensionality reduction, feature selection keeps the original variables rather than transforming them into new representations.

Datasets with too many irrelevant or redundant features can increase computational costs and contribute to overfitting. Selecting useful features can simplify models, reduce training time, improve interpretability, and sometimes improve performance on unseen data.