A game theoretic approach to explain the output of any machine learning model.
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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
A curated list of data mining papers about fraud detection.
A curated list of gradient boosting research papers with implementations.
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
CatBoost a fast, scalable, high performance Gradient Boosting on Decision Trees library. Golang using Cgo for blazing fast inference CatBoost Model 🚀