13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)
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... ------- This video is part of my Introduction of Machine Learning course. Next video: • 13.4.1 Recursive Feature Elimination (L13:... The complete playlist: • Intro to Machine Learning and Statistical ... 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

13.4.1 Recursive Feature Elimination (L13: Feature Selection)

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

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13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)

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13.4.2 Feature Permutation Importance (L13: Feature Selection)

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Uninterrupted Deep Work Mix ~ Immersive Productivity Soundscape ~ Neural Focus Study Music

13.4.4 Sequential Feature Selection (L13: Feature Selection)

Regression Trees, Clearly Explained!!!

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