chriskuchar/RFX
GPU-accelerated Random Forest library with advanced interpretability, proximity analysis, and interactive visualization. Python + C++/CUDA. Scales to 1M+ samples.
This project helps data scientists, analysts, and researchers understand complex relationships in their datasets and uncover hidden patterns. You provide your raw data, and it generates detailed insights like which factors are most influential for each individual case, how samples relate to each other, and interactive visualizations to explore these connections. It's designed for anyone who needs to not only predict outcomes but also deeply interpret the 'why' behind those predictions, especially with larger datasets.
Use this if you need to deeply understand the factors influencing your data and the relationships between individual data points, beyond just getting a prediction, especially on large datasets where traditional methods struggle with memory.
Not ideal if you only need basic predictions without any need for detailed interpretability, proximity analysis, or interactive data exploration, or if you don't have access to a GPU for accelerated processing.
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Language
C++
License
MIT
Category
Last pushed
Dec 04, 2025
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