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

Code repository for the online course Feature Selection for Machine Learning
https://github.com/solegalli/feature-selection-for-machine-learning

data-science feature-selection machine-learning python

Last synced: 3 days ago
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Code repository for the online course Feature Selection for Machine Learning

• Host: GitHub
• URL: https://github.com/solegalli/feature-selection-for-machine-learning
• Owner: solegalli
• License: other
• Created: 2020-01-08T17:08:12.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2024-10-31T10:00:58.000Z (about 2 months ago)
• Last Synced: 2024-12-14T05:03:41.406Z (10 days ago)
• Topics: data-science, feature-selection, machine-learning, python
• Language: Jupyter Notebook
• Homepage: https://www.courses.trainindata.com/p/feature-selection-for-machine-learning
• Size: 3.48 MB
• Stars: 305
• Watchers: 9
• Forks: 337
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
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## Feature Selection for Machine Learning - Code Repository

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

**Launched**: February, 2018

**Updated**: October, 2024

Actively maintained.

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

## Links

- [Online Course](https://www.trainindata.com/p/feature-selection-for-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. Basic Selection Methods + Correlation - Pipeline

3. **Filter Methods: Statistical Methods**

	1. Mutual Information

	2. Chi-square distribution

	3. Anova

	4. Basic Selection Methods + Statistical Methods - Pipeline

   

4. **Filter Methods: Other Methods and Metrics**

	1. Univariate roc-auc, mse, etc

	2. Method used in a KDD competition - 2009

5. **Wrapper Methods**

	1. Step Forward Feature Selection

	2. Step Backward Feature Selection

	3. Exhaustive Feature Selection

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

	1. Logistic Regression Coefficients

	2. Linear Regression Coefficients

	3. Effect of Regularization on Coefficients

	4. Basic Selection Methods + Correlation + Embedded - Pipeline 

7. **Embedded Methods: Lasso**

	1. Lasso 

	2. Basic Selection Methods + Correlation + Lasso - Pipeline 

8. **Embedded Methods: Tree Importance**

	1. Random Forest derived Feature Importance

	2. Tree importance + Recursive Feature Elimination

	3. Basic Selection Methods + Correlation + Tree importance - Pipeline

9. **Hybrid Feature Selection Methods**

	1. Feature Shuffling

	2. Recursive Feature Elimination

	3. Recursive Feature Addition

## Links

- [Online Course](https://www.trainindata.com/p/feature-selection-for-machine-learning)