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.).
MIT License
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In README, link to https://github.com/szilard/talks is dead #55
In the README, there's a link to https://github.com/szilard/talks but it's dead.