feature selection

Feature selection is the process of identifying and choosing the most relevant variables, or "features," from a larger set for analysis or modeling. In simpler terms, imagine trying to solve a puzzle with many pieces; feature selection helps you focus on the few pieces that are most important for completing the picture. This step improves the efficiency and accuracy of models by reducing complexity, minimizing noise, and preventing overfitting, which occurs when a model learns too much from the training data and fails to generalize to new data.