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

Published: 30 December 2021
on channel: Sebastian Raschka

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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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