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.).
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An R 📦 to make testing 😀
Test coverage reports for R
an R package that automates performance testing of ML models and summarizes the results in a dashboard w/ rad visualizations