Wrapper methods are a type of feature selection technique that evaluates the usefulness of a subset of features by using a specific machine learning algorithm to assess their performance. These methods consider the feature selection process as a search problem, where different combinations of features are tested and scored based on their contribution to the predictive accuracy of the model. The goal is to identify the best feature set that improves model performance by wrapping the selected features around the learning algorithm.
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