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.
The documentation can be found here.
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)
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.
FAQs
Implementation of Cyclic Boosting machine learning algorithms
We found that cyclic-boosting demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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