RGF-team/rgf
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.
This library helps machine learning practitioners build highly accurate predictive models by using tabular data as input. It generates a robust model that can often outperform traditional gradient boosting methods, making it suitable for tasks like predicting customer behavior or classifying data. Data scientists and machine learning engineers can use this to enhance their modeling capabilities.
383 stars. No commits in the last 6 months.
Use this if you need to build a predictive model from structured data and are looking for a method that offers strong performance and generalization, potentially exceeding that of gradient boosted trees.
Not ideal if your primary data type is unstructured text or images, or if you require extremely fast model training times for very large datasets where simpler models might suffice.
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Language
C++
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Last pushed
Jan 08, 2022
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