Description: The Wrapper Method is a feature selection technique in the realm of supervised learning that uses a predictive model to evaluate combinations of features. Unlike other selection methods, such as filters, which assess features independently, the Wrapper Method considers the interaction between features by using a specific model to measure the performance of different subsets of features. This approach involves training the model multiple times, each time with a different set of features, and selecting the set that provides the best performance in terms of accuracy, recall, or another relevant metric. The Wrapper Method is particularly useful in situations where there is a high number of features, as it helps identify the most relevant ones for various tasks. However, its downside is that it can be computationally expensive, especially with complex models and large datasets, as each evaluation requires a full model training. Despite this, its ability to capture complex interactions between features makes it a valuable tool in the modeling process, helping to improve the accuracy and interpretability of predictive models.