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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.
Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper.
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:
You may want to get interesting information about RGF from the posts collected in Awesome RGF.
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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.