10. Exhaustive Feature Selection | Wrapper Method |
Welcome to our video on wrapper based feature selection, specifically focusing on exhaustive search! In this tutorial, we'll be exploring a technique for evaluating the performance of all possible combinations of features in a dataset. This method can be particularly useful when working with datasets that contain many irrelevant or redundant features.
We'll be demonstrating how to apply exhaustive search in Python using scikit-learn. We'll start by loading and preparing the dataset, then we'll define a function for evaluating the performance of a model using cross-validation. Next, we'll implement the exhaustive search algorithm and use it to select the top performing feature combination. Finally, we'll evaluate the impact of feature selection on the model's performance.
By the end of this video, you'll have a solid understanding of how to use wrapper based feature selection with exhaustive search and be able to confidently apply it in your own machine learning projects.
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