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 game theoretic approach to explain the output of any machine learning model.

Machine learning, in numpy

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

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.

Fit interpretable models. Explain blackbox machine learning.

[UNMAINTAINED] Automated machine learning for analytics & production

A collection of research papers on decision, classification and regression trees with implementations.

Natural Gradient Boosting for Probabilistic Prediction

A curated list of data mining papers about fraud detection.

A curated list of gradient boosting research papers with implementations.

Tuning hyperparams fast with Hyperband

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

Real time eye tracking for embedded and mobile devices.

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

InfiniteBoost: building infinite ensembles with gradient descent

Open source Machine Learning library written in Java

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

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

Gradient Boosting powered by GPU(NVIDIA CUDA)

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

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

Building Decision Trees From Scratch In Python

Boosted trees in Julia

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Machine learning regression algorithm on cryptocurrency stock price for the next 30 days.