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 Overview L8.1 Logistic Regression as a Single-Layer Neural Network (09:15) • L8.1 Logistic Regression as a Single-Layer... 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 Derivative a... 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 -- Cod... L8.6 Multinomial Logistic Regression / Softmax Regression (17:31) • L8.6 Multinomial Logistic Regression / Sof... L8.7.1 OneHot Encoding and Multi-category Cross Entropy (15:34) • L8.7.1 OneHot Encoding and Multi-category ... L8.7.2 OneHot Encoding and Multi-category Cross Entropy Code Example (15:04) • L8.7.2 OneHot Encoding and Multi-category ... L8.8 Softmax Regression Derivatives for Gradient Descent (19:38) • L8.8 Softmax Regression Derivatives for Gr... L8.9 Softmax Regression Code Example Using PyTorch (25:39) • L8.9 Softmax Regression -- Code Example Us... ------- This video is part of my Introduction of Machine Learning course. Next video: • 13.4.2 Feature Permutation Importance (L13... The complete playlist: • Intro to Machine Learning and Statistical ... A handy overview page with links to the materials: https://sebastianraschka.com/blog/202... ------- If you want to be notified about future videos, please consider subscribing to my channel: / sebastianraschka
13.4.2 Feature Permutation Importance (L13: Feature Selection)
13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)
What I Learned From Implementing LLM Architectures From Scratch (And How to Get Started)
13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)
6.2 Recursive algorithms & Big-O (L06: Decision Trees)
13.4.4 Sequential Feature Selection (L13: Feature Selection)
13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)
Lecture 8: Feature engineering, selection, and regularization – Machine Learning for Engineers
Build an LLM from Scratch 5: Pretraining on Unlabeled Data
7.5 Gradient Boosting (L07: Ensemble Methods)
Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker
If Prime Numbers Become Increasingly Rare, Then Why Do They Keep Showing Up In Pairs?
Feature Selection in Machine Learning with Python - Soledad Galli
Tutorial 2- Feature Selection-How To Drop Features Using Pearson Correlation
5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
40Hz Binaural Gamma Waves - Ultra Deep Concentration
Feature Engineering Techniques For Machine Learning in Python
Feature Engineering Full Course - in 1 Hour | Beginner Level
13.2 Filter Methods for Feature Selection -- Variance Threshold (L13: Feature Selection)
