Implementation of Cyclic Boosting machine learning algorithms
Project description
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
cyclic_boosting-1.0.1.tar.gz
(80.2 kB
view hashes)
Built Distribution
Close
Hashes for cyclic_boosting-1.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | dc7dd966ca8741325eae6fb9d8cbea3cdb4d6a5216f7ecf99fadd14302935adc |
|
MD5 | 82361d7ca027fa27b29717771d68247d |
|
BLAKE2b-256 | 80c3b82f185d9a8256694c91ad7ee0ba8f0aeceb135660a6d0c5b97d29e25148 |