gradient-boosting
There are 1178 repositories under gradient-boosting topic.
shap/shap
A game theoretic approach to explain the output of any machine learning model.
microsoft/LightGBM
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.
ddbourgin/numpy-ml
Machine learning, in numpy
EpistasisLab/tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
catboost/catboost
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.
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
benedekrozemberczki/awesome-decision-tree-papers
A collection of research papers on decision, classification and regression trees with implementations.
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
benedekrozemberczki/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection.
ClimbsRocks/auto_ml
[UNMAINTAINED] Automated machine learning for analytics & production
TorchEnsemble-Community/Ensemble-Pytorch
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
benedekrozemberczki/awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
sberbank-ai-lab/LightAutoML
LAMA - automatic model creation framework
Freemanzxp/GBDT_Simple_Tutorial
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
tensorflow/decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
x4nth055/emotion-recognition-using-speech
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
google/yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
zygmuntz/hyperband
Tuning hyperparams fast with Hyperband
perpetual-ml/perpetual
A self-generalizing gradient boosting machine that doesn't need hyperparameter optimization
serengil/chefboost
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
siboehm/lleaves
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
elucideye/drishti
Real time eye tracking for embedded and mobile devices.
rickiepark/handson-ml
도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
EpistasisLab/tpot2
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
cheng-li/pyramid
Open source Machine Learning library written in Java
Evovest/EvoTrees.jl
Boosted trees in Julia
arogozhnikov/infiniteboost
InfiniteBoost: building infinite ensembles with gradient descent
ml-academy-ai/Machine-Learning-Roadmap
Machine Learning Roadmap for 2025. Step-by-step guide to become a Data Scientist. Covers the best free learning resources from Python basics to Deep Learning and MLOps.
feedzai/fairgbm
Train Gradient Boosting models that are both high-performance *and* Fair!
soda-inria/hazardous
Competing Risks and Survival Analysis
benedekrozemberczki/tigerlily
TigerLily: Finding drug interactions in silico with the Graph.
pfnet-research/autogbt-alt
An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.
Blunde1/agtboost
Adaptive and automatic gradient boosting computations.
bsharchilev/influence_boosting
Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"
serengil/decision-trees-for-ml
Building Decision Trees From Scratch In Python