Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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 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.

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax).

glmark2 is an OpenGL 2.0 and ES 2.0 benchmark

Performance of various open source GBM implementations

Lightweight Decision Trees Framework supporting Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

Ruby Scoring API for PMML

Vulkan benchmark

Show how to perform fast retraining with LightGBM in different business cases

Nanopi M4 RK3399 base minimal image for development (mali fbdev / gbm)

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

LightGBM.jl provides a high-performance Julia interface for Microsoft's LightGBM.

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

Building Decision Trees From Scratch In Python

Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

Faster, better, smarter ecological niche modeling and species distribution modeling

Stacked Gradient Boosting Machines

[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)

This repository covers h2o ai based implementations

Ensemble Learning for Apache Spark 🌲

Math behind all the mainstream tree-based machine learning models

Automatic short-term covid-19 spread prediction by countries and Russian regions

This repository is a tutorial about survival analysis based on advanced machine learning methods including Random Forest, Gradient Boosting Tree and XGBoost. All of them are implemented in R.

😱 Lucurious -> [Library] for building advanced DRM/KMS Vulkan Renderers 😱

Perl wrapper for XGBoost library