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13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)

Опубликовано: 22 Декабрь 2021
на канале: Sebastian Raschka
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Sebastian's books: https://sebastianraschka.com/books/

This video explains how decision trees training can be regarded as an embedded method for feature selection. Then, we will also look at random forest feature importance and go over two different ways it's computed: (a) impurity-based and (b) permutation-based.

Slide link: https://sebastianraschka.com/pdf/lect...

Code link: https://github.com/rasbt/stat451-mach...

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This video is part of my Introduction of Machine Learning course.

Next video:    • 13.4.1 Recursive Feature Elimination ...  

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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If you want to be notified about future videos, please consider subscribing to my channel:    / sebastianraschka  

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