.. MetaSklearn documentation master file, created by
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Welcome to MetaSklearn's documentation!
=======================================

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**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


.. toctree::
   :maxdepth: 4
   :caption: Quick Start:

   pages/quick_start.rst

.. toctree::
   :maxdepth: 4
   :caption: Models API:

   pages/metasklearn.rst

.. toctree::
   :maxdepth: 4
   :caption: Support:

   pages/support.rst


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`