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.).
Hey, thanks for this repository. It's tremendously useful. Would it be possible to maybe add info on how to cite this repository? Maybe sth like:
Best, Simon