BOASF combines Bayesian optimization with adaptive successive filtering and softmax resource allocation to speed up model selection and hyperparameter optimization in automatic machine learning.
Benchmarking Automatic Machine Learning Frameworks
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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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BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
BOASF combines Bayesian optimization with adaptive successive filtering and softmax resource allocation to speed up model selection and hyperparameter optimization in automatic machine learning.