Gbdt
Showing 8 of 8 projects
A scalable, portable, and distributed gradient boosting library for efficient machine learning across multiple languages and platforms.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other machine learning tasks.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other ML tasks.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A fast GPU-accelerated library for training Gradient Boosting Decision Trees (GBDT) and Random Forests.
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
An optimized distributed gradient boosting library for fast and accurate machine learning on large datasets.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
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