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

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.

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.

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

Natural Gradient Boosting for Probabilistic Prediction

[UNMAINTAINED] Automated machine learning for analytics & production

A curated list of data mining papers about fraud detection.

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

A curated list of gradient boosting research papers with implementations.

LAMA - automatic model creation framework

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

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

Tuning hyperparams fast with Hyperband

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

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

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

Real time eye tracking for embedded and mobile devices.