Recursive Feature Elimination (RFE)

Recursive Feature Elimination (RFE) is a method used to improve a model by selecting the most important features (or variables) for making predictions. It works by starting with all features, training the model, and then removing the least important feature. This process repeats—training again and removing the next least important—until only the desired number of features remains. RFE helps simplify models, reduces overfitting, and can improve prediction accuracy by focusing on the most relevant information for the task.