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.

python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision Trees

ThunderGBM: Fast GBDTs and Random Forests on GPUs

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

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).

A self-generalizing gradient boosting machine which doesn't need hyperparameter optimization

An end-to-end machine learning and data mining framework on Hadoop

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见机器学习算法原理与实现

numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法

This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) .

A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)

A java implementation of LightGBM predicting part

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

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

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

A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.

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