Scipediacontent: Scipediacontent moved page Draft Content 125253146 to Olson Moore 2019a

2020-09-23T11:02:27Z

Scipediacontent moved page Draft Content 125253146 to Olson Moore 2019a

Scipediacontent: Created page with " == 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...."

2020-09-23T11:02:24Z

Created page with " == 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...."

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== 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.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_125253146-beopen1156-9831-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

[http://dx.doi.org/10.1007/978-3-030-05318-5_8 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 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://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 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://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://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://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_8.pdf],[https://academic.microsoft.com/#/detail/2947123069 https://academic.microsoft.com/#/detail/2947123069]

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Olson Moore 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 125253146 to Olson Moore 2019a

Scipediacontent: Created page with " == 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...."