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...
-------
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...
-------
If you want to be notified about future videos, please consider subscribing to my channel: / sebastianraschka
Watch video 13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection) online without registration, duration hours minute second in high quality. This video was added by user Sebastian Raschka 30 December 2021, don't forget to share it with your friends and acquaintances, it has been viewed on our site 9,662 once and liked it 161 people.