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 thực hiện GBDT trở về, nhị phân loại cùng với đa phần loại, đem thuật toán lưu trình tình hình cụ thể và tỉ mỉ tiến hành triển lãm giải đọc cũng khả thị hóa, bào đinh giải ngưu mà lý giải 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).

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

A repository contains more than 12 common statistical machine learning algorithm implementations. Thường thấy máy móc học tập thuật toán nguyên lý cùng thực hiện

numpy thực hiện chu chí hoa 《 máy móc học tập 》 thư trung thuật toán cùng mặt khác một ít truyền thống máy móc học tập thuật toán

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

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