Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Code repository for the online course Feature Selection for Machine Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 344 |
| 2026-08-02 | 344 |
| 2026-08-06 | 344 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
 [](https://github.com/solegalli/feature-selection-for-machine-learning/blob/master/LICENSE) [](https://www.trainindata.com/) ## Feature Selection for Machine Learning - Code Repository [<img src="./course-banner.png">](https://www.trainindata.com/p/feature-selection-for-machine-learning) **Launched**: February, 2018 **Updated**: October, 2024 Actively maintained. [<img src="./feature_selection.png" width="248">](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)
Excerpt of 2,429 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:516b54b37f9968b7, llm:Repository description and README: 'Feature Selection for Machine Learning - Code Repository', topics: feature-selection, machine-learning, data-science, python; content lists methods like filter/wrapper/embedded feature selection.
matched fp:516b54b37f9968b7, llm:Repository description and README: 'Feature Selection for Machine Learning - Code Repository', topics: feature-selection, machine-learning, data-science, python; content lists methods like filter/wrapper/embedded feature selection.
matched fp:516b54b37f9968b7, llm:Repository description and README: 'Feature Selection for Machine Learning - Code Repository', topics: feature-selection, machine-learning, data-science, python; content lists methods like filter/wrapper/embedded feature selection.