Recursive Feature Elimination

Recursive Feature Elimination (RFE) is a method for selecting the most important variables in a dataset for making predictions. It works by starting with all features, training a model, and then removing the least important one based on the model's assessment. This process repeats—training the model, removing the weakest feature—until only the most relevant features remain. RFE helps improve model accuracy and simplicity by focusing on the data points that truly matter, eliminating noisy or redundant information that might otherwise hinder performance.