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-mach...
learning-fs21/blob/main/13-feature-selection/05_permutation-importance.ipynb
Using a random feature as a control: https://github.com/rasbt/stat451-mach...
Checking correlated features: https://github.com/rasbt/stat451-mach...
Slides: https://sebastianraschka.com/pdf/lect...
Random forest importance video: • 13.3.2 Decision Trees & Random Forest...
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This video is part of my Introduction of Machine Learning course.
Next video: • 13.4.4 Sequential Feature Selection (...
The complete playlist: • Intro to Machine Learning and Statist...
A handy overview page with links to the materials: https://sebastianraschka.com/blog/202...
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