Uploaded December 2021 | Updated September 2026, 1 week ago

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Sebastian's books: sebastianraschka.com/books

In this video, we start our discussion of wrapper methods for feature selection. In particular, we cover Recursive Feature Elimination (RFE) and see how we can use it in scikit-learn to select features based on linear model coefficients.

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/04_recursive-feature-elimination.ipynb


Logistic regression lectures:

L8.0 Logistic Regression – Lecture Overview (06:28)
youtube.com/watch?v=10PTpRRpRk0

L8.1 Logistic Regression as a Single-Layer Neural Network (09:15)
youtube.com/watch?v=ncZ5iSZekVQ

L8.2 Logistic Regression Loss Function (12:57)
youtube.com/watch?v=GxJe0DZvydM

L8.3 Logistic Regression Loss Derivative and Training (19:57)
youtube.com/watch?v=7rR1L7t2EnA

L8.4 Logits and Cross Entropy (06:47)
youtube.com/watch?v=icQaFxKa_J0

L8.5 Logistic Regression in PyTorch – Code Example (19:02)
youtube.com/watch?v=6igMArA6k3A

L8.6 Multinomial Logistic Regression / Softmax Regression (17:31)
youtube.com/watch?v=L0FU8NFpx4E

L8.7.1 OneHot Encoding and Multi-category Cross Entropy (15:34)
youtube.com/watch?v=4n71-tZ94yk

L8.7.2 OneHot Encoding and Multi-category Cross Entropy Code Example (15:04)
youtube.com/watch?v=5bW0vn4ISqs

L8.8 Softmax Regression Derivatives for Gradient Descent (19:38)
youtube.com/watch?v=aeM-fmcdkXU

L8.9 Softmax Regression Code Example Using PyTorch (25:39)
youtube.com/watch?v=mM6apVBXGEA

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

Next video: youtu.be/VUvShOEFdQo

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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