GitHub - szilard/benchm-ml: 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.). - discu.eu

Hacker News

Comparison of machine learning libraries used for classification https://github.com/szilard/benchm-ml 34 comments 12/8/2015

Linking pages

GitHub - suiji/Arborist: Modular, extensible implementation of the Random Forest (TM) algorithm. https://github.com/suiji/Arborist 0 comments
GitHub - rambler-digital-solutions/criteo-1tb-benchmark: Benchmark of different ML algorithms on Criteo 1TB dataset https://github.com/rambler-digital-solutions/criteo-1tb-benchmark 0 comments
Predicting And Mapping Arrest Types in San Francisco with LightGBM, R, ggplot2 | Max Woolf's Blog http://minimaxir.com/2017/02/predicting-arrests/ 0 comments
Large Scale Decision Forests: Lessons Learned - Sift Engineering Blog : Sift Engineering Blog http://blog.siftscience.com/2015/large-scale-decision-forests-lessons-learned/ 0 comments

Linked pages

GitHub - szilard/GBM-perf: Performance of various open source GBM implementations https://github.com/szilard/GBM-perf 0 comments

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