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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.).
MIT License
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benchmarking with autosklearn (zeroconf) #50

Open Motorrat opened 7 years ago

Motorrat commented 7 years ago

Great initiative, thanks for making this public! You might be interested in extending your benchmarking to the auto-sklearn. https://github.com/automl/auto-sklearn I have created a script that can take in a sparse dataset in the pandas HDFS dataframe .h5 format and run a binary classification on it on multiprocessing cluster with auto-sklearn. https://github.com/Motorrat/autosklearn-zeroconf Myself I will try to duplicate your benchmark, but just in case you are on it you might want to try out yourself.

szilard commented 7 years ago

Thanks @Motorrat for feedback/comments/info. I probably won't have time to extend the benchmark, but contributions are welcome :)