rgf_python
A tree ensemble machine learning method that delivers better results than gradient boosted decision trees on many datasets.
What is rgf_python?
Regularized Greedy Forest (RGF) is a tree ensemble machine learning algorithm that often outperforms traditional gradient boosted decision trees (GBDT) on various datasets. It works by directly optimizing the forest structure with built-in tree-structured regularization, rather than building trees sequentially. The method has been used successfully in Kaggle competitions and academic research.
Data scientists, machine learning engineers, and researchers who need high-performance tree ensemble models for tabular data, particularly those competing in Kaggle or working on problems where GBDT methods are commonly applied.
RGF provides better predictive performance than standard gradient boosting on many datasets due to its direct forest optimization and built-in regularization. The multi-core FastRGF implementation offers speed advantages, and the availability of Python and R wrappers makes it accessible for practical data science workflows.
Overview
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.
Use Cases
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