13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)

Sebastian's books: https://sebastianraschka.com/books/ This video shows code examples for computing permutation importance in mlxtend and scikit-learn. Permutation importance is a model-agnostic, versatile way for computing the importance of features based on a machine learning classifier or regression model. Code notebooks: Wine data example: https://github.com/rasbt/stat451-machine- learning-fs21/blob/main/13-feature-selection/05_permutation-importance.ipynb Using a random feature as a control: https://github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/06_random_feature_as_control.ipynb Checking correlated features: https://github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/07_perm-imp-with-correlated-feats.ipynb Slides: https://sebastianraschka.com/pdf/lecture-notes/stat451fs21/13_feat-sele__slides.pdf Random forest importance video: https://youtu.be/ycyCtxZ0a9w ------- This video is part of my Introduction of Machine Learning course. Next video: https://youtu.be/0vCXcGJg5Bo The complete playlist: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3 A handy overview page with links to the materials: https://sebastianraschka.com/blog/2021/ml-course.html ------- If you want to be notified about future videos, please consider subscribing to my channel: https://youtube.com/c/SebastianRaschka