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arXiv · 1808.06492

Benchmarking Automatic Machine Learning Frameworks

Adithya Balaji·Alexander Allen
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Abstract

AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process. A wide range of techniques is taken to address this, however there does not exist an objective comparison of these techniques. We present a benchmark of current open source AutoML solutions using open source datasets. We test auto-sklearn, TPOT, auto_ml, and H2O's AutoML solution against a compiled set of regression and classification datasets sourced from OpenML and find that auto-sklearn performs the best across classification datasets and TPOT performs the best across regression datasets.

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Adithya Balaji, Alexander Allen. 2018-08-17. Benchmarking Automatic Machine Learning Frameworks. https://arxiv.org/abs/1808.06492

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