Uploaded December 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: 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: github.com/rasbt/stat451-machine-
learning-fs21/blob/main/13-feature-selection/05_permutation-importance.ipynb
Using a random feature as a control: github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/06_random_feature_as_control.ipynb
Checking correlated features: github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/07_perm-imp-with-correlated-feats.ipynb
Slides: sebastianraschka.com/pdf/lecture-notes/stat451fs21/13_feat-sele__slides.pdf
Random forest importance video: youtu.be/ycyCtxZ0a9w
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This video is part of my Introduction of Machine Learning course.
Next video: youtu.be/0vCXcGJg5Bo
The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3
A handy overview page with links to the materials: sebastianraschka.com/blog/2021/ml-course.html
-------
If you want to be notified about future videos, please consider subscribing to my channel: youtube.com/c/SebastianRaschka
Sebastian's books: 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: github.com/rasbt/stat451-machine-
learning-fs21/blob/main/13-feature-selection/05_permutation-importance.ipynb
Using a random feature as a control: github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/06_random_feature_as_control.ipynb
Checking correlated features: github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/07_perm-imp-with-correlated-feats.ipynb
Slides: sebastianraschka.com/pdf/lecture-notes/stat451fs21/13_feat-sele__slides.pdf
Random forest importance video: youtu.be/ycyCtxZ0a9w
-------
This video is part of my Introduction of Machine Learning course.
Next video: youtu.be/0vCXcGJg5Bo
The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3
A handy overview page with links to the materials: sebastianraschka.com/blog/2021/ml-course.html
-------
If you want to be notified about future videos, please consider subscribing to my channel: youtube.com/c/SebastianRaschka
