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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 drop-in replacement for std::list with 293% faster insertion, 57% faster erasure, 17% faster iteration and 77% faster sorting on average. 20-24% speed increase in use-case testing.