Metadata-Version: 2.1
Name: cyclic-boosting
Version: 0.0.4
Author: Blue Yonder GmbH
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: six
Requires-Dist: decorator
Requires-Dist: numba
Requires-Dist: numba-scipy
Requires-Dist: numpy (>=1.12.1)
Requires-Dist: numexpr (>=2.5.2)
Requires-Dist: scipy (>=1.1.0)
Requires-Dist: scikit-learn (>=0.18.2)
Requires-Dist: pandas (>=0.20.3)
Requires-Dist: matplotlib (>=1.5.1)
Requires-Dist: hypothesis

cyclic-boosting
===============

This package contains the implementation of the machine learning algorithm Cyclic Boosting, which is described in [Cyclic Boosting - an explainable supervised machine learning algorithm](https://arxiv.org/abs/2002.03425) and [Demand Forecasting of Individual Probability Density Functions with Machine Learning](https://arxiv.org/abs/2009.07052).

Documentation
-------------

The documentation can be found [here](https://cyclic-boosting.readthedocs.io/en/latest/).

Usage
-----

It can be used in a [scikit-learn](https://scikit-learn.org/stable/)-like fashion, combining a binning method (e.g., [BinNumberTransformer](https://github.com/Blue-Yonder-OSS/cyclic-boosting/blob/main/cyclic_boosting/binning/bin_number_transformer.py)) with a Cyclic Boosting estimator (find all estimators in the [init](https://github.com/Blue-Yonder-OSS/cyclic-boosting/blob/main/cyclic_boosting/__init__.py)). Usage examples can be found in the [integration tests](https://github.com/Blue-Yonder-OSS/cyclic-boosting/blob/main/tests/test_integration.py).
