13.4.2 Feature Permutation Importance (L13: Feature Selection) @SebastianRaschka
13.4.2 Feature Permutation Importance (L13: Feature Selection)  @SebastianRaschka
Uploaded December 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

This video introduces permutation importance, which is a model-agnostic, versatile way for computing the importance of features based on a machine learning classifier or regression model.

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/meTXOuFV-s8

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

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13.4.2 Feature Permutation Importance (L13: Feature Selection)L10.0 Regularization Methods for Neural Networks   Lecture OverviewL6.3 Automatic Differentiation in PyTorch   Code ExampleL14.3: Architecture OverviewDeep Learning News #7 Mar 13 2021L4.5 A Fully Connected (Linear) Layer in PyTorchL10.3 Early StoppingL5.5 (Optional) Calculus Refresher II: GradientsBuild an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate TextL14.3.1.1 VGG16 OverviewL9.3.2 Multilayer Perceptron in PyTorch   Code Example Part 2/3 (Jupyter Notebook)L17.2 Sampling from a Variational Autoencoder
Sebastian Raschka |

13.4.2 Feature Permutation Importance (L13: Feature Selection)

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