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, Dask, 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.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

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

glmark2 is an OpenGL 2.0 and ES 2.0 benchmark

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

A full pipeline AutoML tool for tabular data

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).

Vulkan benchmark

Performance of various open source GBM implementations

Use systemd to allow for standalone operation of kodi.

This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.

Train Gradient Boosting models that are both high-performance *and* Fair!

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Ruby Scoring API for PMML

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

Building Decision Trees From Scratch In Python

PKBoost: Adaptive GBDT for Concept Drift, Built from scratch in Rust, PKBoost manages changing data distributions in fraud detection with a fraud rate of 0.2%. It shows less than 2% degradation under drift. In comparison, XGBoost experiences a 31.8% drop and LightGBM a 42.5% drop

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

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

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

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

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

Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method

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

Math behind all the mainstream tree-based machine learning models

LightGBM + Optuna: Auto train LightGBM directly from CSV files, Auto tune them using Optuna, Auto serve best model using FastAPI. Inspired by Abhishek Thakur's AutoXGB.

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.

A powerful tree-based uplift modeling system.