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