A collection of research papers on decision, classification and regression trees with implementations.
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A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
A curated list of gradient boosting research papers with implementations.