szilard/benchm-ml
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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.).
4 mentions on Hacker News by 4 people: 2 stories, 2 comments · first 2015-08-12 · latest 2017-09-06
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Benchmarking of ML Librariesstory by JPKab · 2017-09-06 · 0 comments
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…and maybe an outline of the code / reasons why. It was not benchmarked by the same person who did https://github.com/szilard/benchm-ml
comment by mikeskim · 2016-03-27 · in Smile – Statistical Machine Intelligence and Learning Engine -
There are plenty of alternatives out there to Spark ML: here is a survey of RF implementations: https://github.com/szilard/benchm-ml/tree/master/z-other-too... There is a whole other world of non stochastic gradient descent based…
comment by barneso · 2016-02-17 · in Running your models in production with TensorFlow Serving -
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Comparison of machine learning libraries used for classificationstory by pzs · 2015-08-12 · 34 comments