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

Опубликовано: 13 Декабрь 2021
на канале: Sebastian Raschka
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Sebastian's books: https://sebastianraschka.com/books/

Without going into the nitty-gritty details behind logistic regression, this lecture explains how/why we can consider an L1 penalty --- a modification of the loss function -- as an embedded feature selection method.

Slides: https://sebastianraschka.com/pdf/lect...

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

Links to the logistic regression videos I referenced:
https://sebastianraschka.com/blog/202...

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

Next video:    • 13.3.2 Decision Trees & Random Forest...  

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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