Welcome to MetaSklearn’s documentation!
MetaSklearn is a flexible and extensible Python library that brings metaheuristic optimization to hyperparameter tuning of scikit-learn models. It provides a seamless interface to optimize hyperparameters using nature-inspired algorithms from the [Mealpy](https://github.com/thieu1995/mealpy) library. It is designed to be user-friendly and efficient, making it easy to integrate into your machine learning workflow.
Free software: GNU General Public License (GPL) V3 license
Provided Searcher: MetaSearchCV
Total Metaheuristic-based Scikit-Learn Regressor: > 200 Models
Total Metaheuristic-based Scikit-Learn Classifier: > 200 Models
Supported performance metrics: >= 67 (47 regressions and 20 classifications)
Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)
Documentation: https://metasklearn.readthedocs.io
Python versions: >= 3.8.x
Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics
Quick Start:
Models API:
Support: