FlorinAndrei/fast_feature_selection
Genetic algorithms and CMA-ES (covariance matrix adaptation evolution strategy) for efficient feature selection
This project helps data scientists and machine learning engineers build more accurate and efficient regression models. By intelligently selecting the most important inputs (features) from a larger dataset, it helps you get better predictive results with less computational effort. It takes a dataset with many potential features and outputs a smaller, optimized set of features ready for model training.
No commits in the last 6 months.
Use this if you are a data scientist working with regression models and need an automated way to identify the most impactful features from a large set of candidates, especially when computation time is a concern.
Not ideal if you are a business user or analyst looking for a no-code solution, as this project requires familiarity with Python and machine learning workflows.
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Last pushed
Aug 11, 2025
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