Wrapper methods involve three main steps: 1. Feature Subset Selection: Different subsets of features are generated. 2. Model Training: A predictive model is trained on each subset. 3. Evaluation: The performance of each model is evaluated using a predefined metric, such as accuracy or AUC (Area Under the Curve).
The process is iterative, and the subset that yields the best performance is selected. Common wrapper methods include forward selection, backward elimination, and recursive feature elimination (RFE).
