Uploaded December 2021 | Updated September 2026, 2 weeks ago

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Sebastian's books: 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: sebastianraschka.com/pdf/lecture-notes/stat451fs21/13_feat-sele__slides.pdf

Code: github.com/rasbt/stat451-machine-learning-fs21/blob/main/13-feature-selection/02_lasso-path.ipynb

Links to the logistic regression videos I referenced:
sebastianraschka.com/blog/2021/dl-course.html#l08-multinomial-logistic-regression--softmax-regression

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

Next video: youtu.be/ycyCtxZ0a9w

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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