Scipediacontent: Scipediacontent moved page Draft Content 479141954 to Kidd et al 2016a

2021-01-25T10:35:22Z

Scipediacontent moved page Draft Content 479141954 to Kidd et al 2016a

Scipediacontent: Created page with " == Abstract == Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business..."

2021-01-25T10:35:19Z

Created page with " == Abstract == Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business..."

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== Abstract ==

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. 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 a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.

== Original document ==

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

* [http://arxiv.org/abs/1601.07925 http://arxiv.org/abs/1601.07925]

* [http://arxiv.org/pdf/1601.07925 http://arxiv.org/pdf/1601.07925]

* [http://dx.doi.org/10.1007/978-3-319-31204-0_9 http://dx.doi.org/10.1007/978-3-319-31204-0_9]

* [http://link.springer.com/content/pdf/10.1007/978-3-319-31204-0_9 http://link.springer.com/content/pdf/10.1007/978-3-319-31204-0_9],
: [http://dx.doi.org/10.1007/978-3-319-31204-0_9 http://dx.doi.org/10.1007/978-3-319-31204-0_9] under the license http://www.springer.com/tdm

* [https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9 https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9],
: [https://dblp.uni-trier.de/db/journals/corr/corr1601.html#OlsonUALKM16 https://dblp.uni-trier.de/db/journals/corr/corr1601.html#OlsonUALKM16],
: [https://rd.springer.com/chapter/10.1007/978-3-319-31204-0_9 https://rd.springer.com/chapter/10.1007/978-3-319-31204-0_9],
: [https://dx.doi.org/10.1007/978-3-319-31204-0_9 https://dx.doi.org/10.1007/978-3-319-31204-0_9],
: [http://dx.doi.org/10.1007/978-3-319-31204-0_9 http://dx.doi.org/10.1007/978-3-319-31204-0_9],
: [https://doi.org/10.1007/978-3-319-31204-0_9 https://doi.org/10.1007/978-3-319-31204-0_9],
: [https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9/fulltext.html https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9/fulltext.html],
: [https://academic.microsoft.com/#/detail/2338065342 https://academic.microsoft.com/#/detail/2338065342]

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Kidd et al 2016a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 479141954 to Kidd et al 2016a

Scipediacontent: Created page with " == Abstract == Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business..."