juampamuc / cyclic-boosting

implementation of Cyclic Boosting machine learning algorithms

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 and Demand Forecasting of Individual Probability Density Functions with Machine Learning.

Documentation

The documentation can be found here.

Quickstart

pip install cyclic-boosting

from cyclic_boosting.pipelines import pipeline_CBPoissonRegressor
CB_est = pipeline_CBPoissonRegressor()
CB_est.fit(X_train, y)
yhat = CB_est.predict(X_test)

Usage

It can be used in a scikit-learn-like fashion, combining a binning method (e.g., BinNumberTransformer) with a Cyclic Boosting estimator (find all estimators in the init). Usage examples can be found in the integration tests.