Sebastian Raschka
@sebastianraschka·88.6K subscribers·307 videos
13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)
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December 31, 2021
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27:38Now PlayingSebastian's 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
learning-fs21/blob/main/13-feature-selection/05_permutation-importance.ipynb
Using a random feature as a control
Checking correlated features
Random forest importance video
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This video is part of my Introduction of Machine Learning course.
The complete playlist
A handy overview page with links to the materials
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