feature-selection

Feature selection is an essential step in the machine learning pipeline, where the goal is to identify and retain the most informative features while eliminating redundant or irrelevant ones. This process improves model performance by reducing complexity, enhancing generalization, and speeding up training times. Techniques for feature selection include filtering, wrapper methods, and embedded methods.