https://github.com/solegalli/feature-selection-in-machine-learning-book

Code repository for the book feature selection in machine learning
https://github.com/solegalli/feature-selection-in-machine-learning-book

data-science feature-selection machine-learning python

Last synced: 12 days ago
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Code repository for the book feature selection in machine learning

• Host: GitHub
• URL: https://github.com/solegalli/feature-selection-in-machine-learning-book
• Owner: solegalli
• License: other
• Created: 2022-06-22T19:03:09.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-27T16:28:06.000Z (7 months ago)
• Last Synced: 2024-10-04T13:42:24.294Z (about 1 month ago)
• Topics: data-science, feature-selection, machine-learning, python
• Language: Jupyter Notebook
• Homepage: https://leanpub.com/feature-selection-in-machine-learning
• Size: 4.27 MB
• Stars: 23
• Watchers: 2
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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## Feature Selection in Machine Learning Book - Code Repository

Published August, 2022

[](https://leanpub.com/feature-selection-in-machine-learning)

## Links

- [Book](https://leanpub.com/feature-selection-in-machine-learning)

## Table of Contents

1. **Basic Selection Methods**

	1. Removing Constant Features

	2. Removing Quasi-Constant Features

	3. Removing Duplicated Features

2. **Correlation Feature Selection**

	1. Removing Correlated Features 

	2. Smart Correlation

3. **Filter Methods: Univariate Statistical Methods**

	1. Chi-square distribution

	2. Anova

	3. Correlation

	4. Mutual information

4. **Univariate Methods**

	1. Single feature classifier

	2. Target mean encoding

5. **Wrapper Methods**

	1. Exhaustive Feature Selection

	2. Step Forward Feature Selection

	3. Step Backward Feature Selection 

6. **Embedded Methods: Linear Model Coefficients**

	1. Lasso

	2. Decision tree feature importance

	3. Recursive feature elimination based on importance 

7. **Hybrid Feature Selection Methods**

	1. Feature Shuffling

	2. Recursive Feature Elimination

	3. Recursive Feature Addition