Popular GitHub Repositories for Gbdt

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.

CTR prediction model based on spark(LR, GBDT, DNN)

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 repository contains more than 12 common statistical machine learning algorithm implementations. 常见10余种机器学习算法原理与实现及视频讲解。@月来客栈 出品

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

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

《统计学习方法》与常见机器学习模型(GBDT/XGBoost/lightGBM/FM/FFM)的原理讲解与python和类库实现

Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark

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

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

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

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

Programmable Decision Tree Framework

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

Save Your Time ! Automated Interpretable Machine Learning Workflow

My simplest implementations of common ML algorithms

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost.

[SIGMOD 2026] DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search.