rodrigo-arenas/Sklearn-genetic-opt
ML hyperparameters tuning and features selection, using evolutionary algorithms.
This tool helps machine learning practitioners fine-tune their models and select the most relevant features using techniques inspired by natural evolution. You provide your dataset and a scikit-learn model, and it outputs the optimal set of model settings (hyperparameters) and/or the best subset of features, leading to improved model performance. It's designed for data scientists and ML engineers building predictive models.
356 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to optimize the performance of your scikit-learn models by finding the best hyperparameters and/or selecting crucial features, especially when traditional methods like Grid Search are too slow or inefficient.
Not ideal if you prefer simpler, exhaustive search methods for hyperparameter tuning or feature selection, or if your models are not based on the scikit-learn framework.
Stars
356
Forks
89
Language
Python
License
MIT
Category
Last pushed
Sep 13, 2025
Commits (30d)
0
Dependencies
4
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