13.4.1 Recursive Feature Elimination (L13: Feature Selection)
Sebastian's books: https://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: https://sebastianraschka.com/pdf/lect...
Code: https://github.com/rasbt/stat451-mach...
Logistic regression lectures:
L8.0 Logistic Regression – Lecture Overview (06:28)
• L8.0 Logistic Regression -- Lecture O...
L8.1 Logistic Regression as a Single-Layer Neural Network (09:15)
• L8.1 Logistic Regression as a Single-...
L8.2 Logistic Regression Loss Function (12:57)
• L8.2 Logistic Regression Loss Function
L8.3 Logistic Regression Loss Derivative and Training (19:57)
• L8.3 Logistic Regression Loss Derivat...
L8.4 Logits and Cross Entropy (06:47)
• L8.4 Logits and Cross Entropy
L8.5 Logistic Regression in PyTorch – Code Example (19:02)
• L8.5 Logistic Regression in PyTorch -...
L8.6 Multinomial Logistic Regression / Softmax Regression (17:31)
• L8.6 Multinomial Logistic Regression ...
L8.7.1 OneHot Encoding and Multi-category Cross Entropy (15:34)
• L8.7.1 OneHot Encoding and Multi-cate...
L8.7.2 OneHot Encoding and Multi-category Cross Entropy Code Example (15:04)
• L8.7.2 OneHot Encoding and Multi-cate...
L8.8 Softmax Regression Derivatives for Gradient Descent (19:38)
• L8.8 Softmax Regression Derivatives f...
L8.9 Softmax Regression Code Example Using PyTorch (25:39)
• L8.9 Softmax Regression -- Code Examp...
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This video is part of my Introduction of Machine Learning course.
Next video: • 13.4.2 Feature Permutation Importance...
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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