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and R tests](https://github.com/RGF-team/rgf/workflows/Python%20and%20R%20tests/badge.svg?branch=master)](https://github.com/RGF-team/rgf/actions)\n[![DOI](https://zenodo.org/badge/DOI/10.1109/TPAMI.2013.159.svg)](https://doi.org/10.1109/TPAMI.2013.159)\n[![arXiv.org](https://img.shields.io/badge/arXiv-1109.0887-b31b1b.svg)](https://arxiv.org/abs/1109.0887)\n[![Python Versions](https://img.shields.io/pypi/pyversions/rgf_python.svg)](https://pypi.org/project/rgf_python)\n[![PyPI Version](https://img.shields.io/pypi/v/rgf_python.svg)](https://pypi.org/project/rgf_python)\n[![CRAN Version](https://r-pkg.org/badges/version/RGF)](https://cran.r-project.org/package=RGF)\n\n# Regularized Greedy Forest\n\nRegularized Greedy Forest (RGF) is a tree ensemble machine learning method described in [this paper](https://arxiv.org/abs/1109.0887).\nRGF can deliver better results than gradient boosted decision trees (GBDT) on a number of datasets and it has been used to win a few Kaggle 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