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

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• Updated Nov 10, 2026
• Jupyter Notebook

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.

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• Updated Nov 12, 2026
• C++

Machine learning, in numpy

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• Updated Oct 29, 2023
• Python

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

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• Updated Sep 11, 2025
• Jupyter Notebook

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.

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• Updated Nov 12, 2026
• C++

Fit interpretable models. Explain blackbox machine learning.

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• Updated Nov 9, 2026
• C++

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

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• Updated Dec 28, 2025
• Python

Natural Gradient Boosting for Probabilistic Prediction

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• Updated Nov 10, 2026
• Jupyter Notebook

A curated list of data mining papers about fraud detection.

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• Updated Jan 5, 2026
• Python

[UNMAINTAINED] Automated machine learning for analytics & production

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• Updated Feb 10, 2021
• Python

A curated list of gradient boosting research papers with implementations.

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• Updated Jan 5, 2026
• Python

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

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• Updated Jun 16, 2024
• Python

LAMA - automatic model creation framework

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• Updated Apr 14, 2022
• Python

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

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• Updated Jun 15, 2019
• Python

Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R

python rust data-science machine-learning kaggle gbdt gbm gradient-boosting gradient-boosted-trees gradient-boosting-decision-trees

• Updated Nov 11, 2026
• Rust

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

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• Updated May 19, 2026
• Python

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

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• Updated Apr 16, 2026
• Python

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

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• Updated Nov 3, 2023
• Python

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

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• Updated Nov 11, 2026
• C++

Tuning hyperparams fast with Hyperband

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• Updated Aug 15, 2018
• Python