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JMLR: Workshop and Conference Proceedings 64:66 74, 2016 ICML 2016 AutoML WorkshopTPOT: A Tree-based Pipeline Optimization Toolfor automating machine LearningRandal S. H. for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USAA bstractAs data science becomes more mainstream, there will be an ever-growing demand for datascience tools that are more accessible, flexible, and scalable. In response to this demand,automated machine learning (AutoML) researchers have begun building systems that auto-mate the process of designing and optimizing machine learning pipelines. In this paper wepresent TPOT , an open source genetic programming-based AutoML system that op-timizes a series of feature preprocessors and machine learning models with the goal of max-imizing classification accuracy on a supervised classification task. We benchmark TPOTon a series of 150 supervised classification tasks and find that it significantly outperformsa basic machine learning analysis in 21 of them, while experiencing minimal degradationin accuracy on 4 of the benchmarks all without any domain knowledge nor human such, GP-based AutoML systems show considerable promise in the AutoML :automated machine learning , hyperparameter Optimization , Pipeline optimiza-tion, genetic programming, Pareto Optimization , data science, Python1.
JMLR: Workshop and Conference Proceedings 64:66{74, 2016 ICML 2016 AutoML Workshop TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning
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