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rgf

★ 382 Open GitHub ↗

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

// repository documentation

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# Regularized Greedy Forest

Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in [this paper](https://arxiv.org/abs/1109.0887).
RGF 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 competitions.
Unlike the traditional boosted decision tree approach, RGF works directly with the underlying forest structure.
RGF integrates two ideas: one is to include tree-structured regularization into the learning formulation; and the other is to employ the fully-corrective regularized greedy algorithm.

This repository contains the following implementations of the RGF algorithm:

- [RGF](https://github.com/RGF-team/rgf/tree/master/RGF): original implementation from the paper;
- [FastRGF](https://github.com/RGF-team/rgf/tree/master/FastRGF): multi-core implementation with some simplifications;
- [rgf_python](https://github.com/RGF-team/rgf/tree/master/python-package): wrapper of both RGF and FastRGF implementations for Python;
- [R package](https://github.com/RGF-team/rgf/tree/master/R-package): wrapper of rgf_python for R.

You may want to get interesting information about RGF from the posts collected in [Awesome RGF](https://github.com/RGF-team/rgf/blob/master/AWESOME_RGF.md ).

rgf

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