Scipediacontent: Scipediacontent moved page Draft Content 433143580 to Urbanowicz et al 2016a

2021-01-28T16:15:56Z

Scipediacontent moved page Draft Content 433143580 to Urbanowicz et al 2016a

Scipediacontent: Created page with " == Abstract == the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this..."

2021-01-28T16:15:51Z

Created page with " == Abstract == the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this..."

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== Abstract ==

the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet made from reviewer commen

== Original document ==

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

* [http://arxiv.org/abs/1603.06212 http://arxiv.org/abs/1603.06212]

* [http://dl.acm.org/ft_gateway.cfm?id=2908918&type=pdf http://dl.acm.org/ft_gateway.cfm?id=2908918&type=pdf]

* [http://dx.doi.org/10.1145/2908812.2908918 http://dx.doi.org/10.1145/2908812.2908918]

* [https://arxiv.org/abs/1603.06212 https://arxiv.org/abs/1603.06212],
: [https://dblp.uni-trier.de/db/journals/corr/corr1603.html#OlsonBUM16 https://dblp.uni-trier.de/db/journals/corr/corr1603.html#OlsonBUM16],
: [https://dl.acm.org/citation.cfm?id=2908918 https://dl.acm.org/citation.cfm?id=2908918],
: [https://dl.acm.org/ft_gateway.cfm?id=2908918&ftid=1769913&dwn=1 https://dl.acm.org/ft_gateway.cfm?id=2908918&ftid=1769913&dwn=1],
: [https://dl.acm.org/citation.cfm?doid=2908812.2908918 https://dl.acm.org/citation.cfm?doid=2908812.2908918],
: [https://doi.org/10.1145/2908812.2908918 https://doi.org/10.1145/2908812.2908918],
: [https://doi.acm.org/10.1145/2908812.2908918 https://doi.acm.org/10.1145/2908812.2908918],
: [https://academic.microsoft.com/#/detail/2309832917 https://academic.microsoft.com/#/detail/2309832917]

* [http://dl.acm.org/ft_gateway.cfm?id=2908918&ftid=1769913&dwn=1 http://dl.acm.org/ft_gateway.cfm?id=2908918&ftid=1769913&dwn=1],
: [http://dx.doi.org/10.1145/2908812.2908918 http://dx.doi.org/10.1145/2908812.2908918] under the license http://www.acm.org/publications/policies/copyright_policy#Background

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Urbanowicz et al 2016a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 433143580 to Urbanowicz et al 2016a

Scipediacontent: Created page with " == Abstract == the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this..."