Feature Selection

Feature selection aims to identify the variables that truly carry useful information instead of blindly using every available column. Weak or irrelevant features not only increase computational cost, but can also make the model more sensitive to noise. Filter, wrapper, and embedded methods offer different strategies for this process. Well-executed feature selection can improve performance, interpretability, and overall stability. More columns do not automatically mean a better model.