Wrapper Methods
Wrapper methods are a feature selection method in which a subset of features is selected by training a model iteratively with different combinations of features. In each iteration, the model is trained on a subset of features and evaluated on a validation set. The subset of features that results in the best performance on the validation set is selected. This process is repeated until the desired number of features is obtained. Wrapper methods are computationally expensive but can result in better performance than other feature selection methods because they take into account the interaction between features.
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