The Top 21 Gradient Boosting Open Source Projects

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The Top 21 Gradient Boosting Open Source Projects

Categories > Machine Learning > Gradient Boosting

Lightgbm ⭐11,869

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.

Numpy Ml ⭐9,115

Machine learning, in numpy

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

Catboost ⭐5,544

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.

Interpret ⭐3,281

Fit interpretable models. Explain blackbox machine learning.

Auto_ml ⭐1,504

[UNMAINTAINED] Automated machine learning for analytics & production

Awesome Decision Tree Papers ⭐1,497

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

Natural Gradient Boosting for Probabilistic Prediction

Awesome Fraud Detection Papers ⭐689

A curated list of data mining papers about fraud detection.

Awesome Gradient Boosting Papers ⭐621

A curated list of gradient boosting research papers with implementations.

Hyperband ⭐541

Tuning hyperparams fast with Hyperband

Gbdt_simple_tutorial ⭐466

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.

Infiniteboost ⭐178

InfiniteBoost: building infinite ensembles with gradient descent

Open source Machine Learning library written in Java

Machine Learning Workflow With Python ⭐157

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

Chefboost ⭐137

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

Emotion Recognition Using Speech ⭐111

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

Arboretum ⭐64

Gradient Boosting powered by GPU(NVIDIA CUDA)

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