Metadata-Version: 2.1
Name: lcensemble
Version: 0.3.2
Summary: Local Cascade Ensemble package
Home-page: https://lce.readthedocs.io/en/latest/
Download-URL: https://github.com/LocalCascadeEnsemble/LCE
Maintainer: Kevin Fauvel
Maintainer-email: kfauvel.lce@gmail.com
License: Apache-2.0
Project-URL: Documentation, https://lce.readthedocs.io/en/latest/
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: docs
License-File: LICENSE



|CircleCI|_ |CodeCov|_ |ReadTheDocs|_ |PyPIversion|_ |PyPIpythonversion|_ |Style|_ |License|_ 



.. |CircleCI| image:: https://circleci.com/gh/LocalCascadeEnsemble/LCE/tree/main.svg?style=shield

.. _CircleCI: https://circleci.com/gh/LocalCascadeEnsemble/LCE/tree/main



.. |CodeCov| image:: https://codecov.io/gh/LocalCascadeEnsemble/LCE/branch/main/graph/badge.svg?token=VTA64P4GTF

.. _CodeCov: https://codecov.io/gh/LocalCascadeEnsemble/LCE

   

.. |ReadTheDocs| image:: https://readthedocs.org/projects/lce/badge/?version=latest

.. _ReadTheDocs: https://lce.readthedocs.io/en/latest/?badge=latest



.. |PyPIversion| image:: https://badge.fury.io/py/lcensemble.svg

.. _PyPIversion: https://pypi.python.org/pypi/lcensemble/



.. |PyPIpythonversion| image:: https://img.shields.io/pypi/pyversions/lcensemble.svg

.. _PyPIpythonversion: https://pypi.python.org/pypi/lcensemble/



.. |Style| image:: https://img.shields.io/badge/code%20style-black-000000.svg

.. _Style: https://github.com/psf/black



.. |License| image:: https://img.shields.io/github/license/LocalCascadeEnsemble/LCE.svg

.. _License: https://pypi.python.org/pypi/lcensemble/





   

| **Local Cascade Ensemble (LCE)** is a *high-performing*, *scalable* and *user-friendly* machine learning method for the general tasks of **Classification** and **Regression**.

| In particular, LCE:

 

- Enhances the prediction performance of Random Forest and XGBoost by combining their strengths and adopting a complementary diversification approach

- Supports parallel processing to ensure scalability

- Handles missing data by design

- Adopts scikit-learn API for the ease of use

- Adheres to scikit-learn conventions to allow interaction with scikit-learn pipelines and model selection tools

- Is released in open source and commercially usable - Apache 2.0 license



An article introducing LCE and illustrative code examples has been published in `Towards Data Science <https://towardsdatascience.com/random-forest-or-xgboost-it-is-time-to-explore-lce-2fed913eafb8?source=friends_link&sk=8cba14ad36f7662d07e842d03944a316>`_.





Installation

~~~~~~~~~~~~



LCE package can be installed using ``pip``::



	pip install lcensemble





Documentation

~~~~~~~~~~~~~



LCE documentation, including API documentation and general examples, can be found `here <https://lce.readthedocs.io/en/latest/>`_.





Reference

~~~~~~~~~



The full information about LCE design and evaluation can be found in the associated `journal paper <https://hal.inria.fr/hal-03599214/document>`_:



.. [1] Fauvel, K., E. Fromont, V. Masson, P. Faverdin and A. Termier. XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification. Data Mining and Knowledge Discovery, 36(3):917–957, 2022

