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 machine-learning-tutorials python

Last synced: 2 months 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 (about 3 years ago)
• Default Branch: main
• Last Pushed: 2025-02-04T10:43:11.000Z (5 months ago)
• Last Synced: 2025-03-31T00:04:29.650Z (4 months ago)
• Topics: data-science, feature-selection, machine-learning, machine-learning-tutorials, python
• Language: Jupyter Notebook
• Homepage: https://www.trainindata.com/p/feature-selection-in-machine-learning-book
• Size: 6.38 MB
• Stars: 28
• Watchers: 2
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
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## Feature Selection in Machine Learning Book - Code Repository

- Published: August, 2022

- Updated: February, 2025

[](https://www.trainindata.com/p/feature-selection-in-machine-learning-book)

## Links

- [Book](https://www.trainindata.com/p/feature-selection-in-machine-learning-book)

## 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. **Statistical methods**

	1. Chi-square distribution

	2. Anova

	3. Correlation

	4. Mutual information

4. **Univariate Methods**

	1. Single feature classifiers / regressors

	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

7. **Recursive Feature Elimination**

	1. RFE - embedded importance

	2. RFE - model performance

8. **Alternative Feature Selection Methods**

	1. Feature Shuffling

	3. Recursive Feature Addition

	4. Probe Features

	5. MRMR

- [Book](https://www.trainindata.com/p/feature-selection-in-machine-learning-book)