gradient-boosting

A game theoretic approach to explain the output of any machine learning model.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Fit interpretable models. Explain blackbox machine learning.

A collection of research papers on decision, classification and regression trees with implementations.

Natural Gradient Boosting for Probabilistic Prediction

A curated list of data mining papers about fraud detection.

A curated list of gradient boosting research papers with implementations.

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.