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Sebastian's books: sebastianrasch...
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/ras...
learning-fs21/blob/main/13-feature-selection/05_permutation-importance.ipynb
Using a random feature as a control: github.com/ras...
Checking correlated features: github.com/ras...
Slides: sebastianrasch...
Random forest importance video: • 13.3.2 Decision Trees ...
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This video is part of my Introduction of Machine Learning course.
Next video: • 13.4.4 Sequential Feat...
The complete playlist: • Intro to Machine Learn...
A handy overview page with links to the materials: sebastianrasch...
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