13.4.1 Recursive Feature Elimination (L13: Feature Selection)

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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.

Logistic regression lectures:

L8.0 Logistic Regression – Lecture Overview (06:28)

L8.1 Logistic Regression as a Single-Layer Neural Network (09:15)

L8.2 Logistic Regression Loss Function (12:57)

L8.3 Logistic Regression Loss Derivative and Training (19:57)

L8.4 Logits and Cross Entropy (06:47)

L8.5 Logistic Regression in PyTorch – Code Example (19:02)

L8.6 Multinomial Logistic Regression / Softmax Regression (17:31)

L8.7.1 OneHot Encoding and Multi-category Cross Entropy (15:34)

L8.7.2 OneHot Encoding and Multi-category Cross Entropy Code Example (15:04)

L8.8 Softmax Regression Derivatives for Gradient Descent (19:38)

L8.9 Softmax Regression Code Example Using PyTorch (25:39)

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

The complete playlist

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