13.4.1 Recursive Feature Elimination (L13: Feature Selection)

Sebastian Raschka · Beginner ·📐 ML Fundamentals ·4y ago

Skills: ML Maths Basics80%

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/lecture-notes/stat451fs21/13_feat-sele__slides.pdf Code: https://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) https://www.youtube.com/watch?v=10PTpRRpRk0 L8.1 Logistic Regression as a Single-Layer Neural Network (09:15) https://www.youtube.com/watch?v=ncZ5iSZekVQ L8.2 Logistic Regression Loss Function (12:57) https://www.youtube.com/watch?v=GxJe0DZvydM L8.3 Logistic Regression Loss Derivative and Training (19:57) https://www.youtube.com/watch?v=7rR1L7t2EnA L8.4 Logits and Cross Entropy (06:47) https://www.youtube.com/watch?v=icQaFxKa_J0 L8.5 Logistic Regression in PyTorch – Code Example (19:02) https://www.youtube.com/watch?v=6igMArA6k3A L8.6 Multinomial Logistic Regression / Softmax Regression (17:31) https://www.youtube.com/watch?v=L0FU8NFpx4E L8.7.1 OneHot Encoding and Multi-category Cross Entropy (15:34) https://www.youtube.com/watch?v=4n71-tZ94yk L8.7.2 OneHot Encoding and Multi-category Cross Entropy Code Example (15:04) https://www.youtube.com/watch?v=5bW0vn4ISqs L8.8 Softmax Regression Derivatives for Gradient Descent (19:38) https://www.youtube.com/watch?v=aeM-fmcdkXU L8.9 Softmax Regression Code Example Using PyTorch (25:39) https://www.youtube.com/watch?v=mM6apVBXGEA ------- This video is part of my Introduction of Machine Learning course. Next video: https://youtu.be/VUvShOEFdQo The complete playlist: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3 A handy overview page with links to the materials: https://sebast

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1.2 What is Machine Learning (L01: What is Machine Learning)

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1.3 Categories of Machine Learning (L01: What is Machine Learning)

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1.4 Notation (L01: What is Machine Learning)

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1.1 Course overview (L01: What is Machine Learning)

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1.5 ML application (L01: What is Machine Learning)

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1.6 ML motivation (L01: What is Machine Learning)

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2.1 Introduction to NN (L02: Nearest Neighbor Methods)

2.1 Introduction to NN (L02: Nearest Neighbor Methods)

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2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)

2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)

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2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)

2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)

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2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)

2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)

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2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)

2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)

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2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)

2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)

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3.1 (Optional) Python overview

3.1 (Optional) Python overview

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3.2 (Optional) Python setup

3.2 (Optional) Python setup

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3.3 (Optional) Running Python code

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4.1 Intro to NumPy (L04: Scientific Computing in Python)

4.1 Intro to NumPy (L04: Scientific Computing in Python)

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4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)

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4.4 NumPy Broadcasting (L04: Scientific Computing in Python)

4.4 NumPy Broadcasting (L04: Scientific Computing in Python)

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4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)

4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)

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4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)

4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)

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4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)

4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)

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4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)

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4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)

4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)

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4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)

4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)

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4.10 Matplotlib (L04: Scientific Computing in Python)

4.10 Matplotlib (L04: Scientific Computing in Python)

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5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)

5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)

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5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)

5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)

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5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)

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5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)

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5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)

5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)

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5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)

5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)

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6.1 Intro to Decision Trees (L06: Decision Trees)

6.1 Intro to Decision Trees (L06: Decision Trees)

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6.2 Recursive algorithms & Big-O (L06: Decision Trees)

6.2 Recursive algorithms & Big-O (L06: Decision Trees)

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6.3 Types of decision trees (L06: Decision Trees)

6.3 Types of decision trees (L06: Decision Trees)

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6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)

6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)

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6.6 Improvements & dealing with overfitting (L06: Decision Trees)

6.6 Improvements & dealing with overfitting (L06: Decision Trees)

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6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)

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7.1 Intro to ensemble methods (L07: Ensemble Methods)

7.1 Intro to ensemble methods (L07: Ensemble Methods)

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7.2 Majority Voting (L07: Ensemble Methods)

7.2 Majority Voting (L07: Ensemble Methods)

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7.3 Bagging (L07: Ensemble Methods)

7.3 Bagging (L07: Ensemble Methods)

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7.4 Boosting and AdaBoost (L07: Ensemble Methods)

7.4 Boosting and AdaBoost (L07: Ensemble Methods)

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7.5 Gradient Boosting (L07: Ensemble Methods)

7.5 Gradient Boosting (L07: Ensemble Methods)

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7.6 Random Forests (L07: Ensemble Methods)

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7.7 Stacking (L07: Ensemble Methods)

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8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)

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8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)

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8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

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