Latest revision as of 12:02, 23 September 2020

Abstract

As data science becomes increasingly mainstream, there will be an ever-growing demand for data science tools that are more accessible, flexible, and scalable. In response to this demand, automated machine learning (AutoML) researchers have begun building systems that automate the process of designing and optimizing machine learning pipelines. In this chapter we present TPOT v0.3, an open source genetic programming-based AutoML system that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification task. We benchmark TPOT on a series of 150 supervised classification tasks and find that it significantly outperforms a basic machine learning analysis in 21 of them, while experiencing minimal degradation in accuracy on 4 of the benchmarks—all without any domain knowledge nor human input. As such, genetic programming-based AutoML systems show considerable promise in the AutoML domain.

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http://dx.doi.org/10.1007/978-3-030-05318-5_8

https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_8.pdf under the license cc-by

http://link.springer.com/content/pdf/10.1007/978-3-030-05318-5_8,http://dx.doi.org/10.1007/978-3-030-05318-5_8 under the license https://creativecommons.org/licenses/by/4.0

https://link.springer.com/chapter/10.1007/978-3-030-05318-5_8,https://rd.springer.com/chapter/10.1007/978-3-030-05318-5_8,https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_8.pdf,https://academic.microsoft.com/#/detail/2947123069