Scipediacontent: Scipediacontent moved page Draft Content 397855286 to Cousins et al 2018a

2021-02-01T22:27:02Z

Scipediacontent moved page Draft Content 397855286 to Cousins et al 2018a

Scipediacontent: Created page with " == Abstract == Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data..."

2021-02-01T22:26:58Z

Created page with " == Abstract == Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data..."

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== Abstract ==

Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data sets and curating machine learning models are limited in a number of fundamental ways. First, existing tools require well-trained data scientists to select the appropriate techniques to build models and to evaluate their outcomes. Second, existing tools require heavy data preparation steps and are often too slow to give interactive feedback to domain experts in the model building process, severely limiting the possible interactions. Third, current tools do not provide adequate analysis of statistical risk factors in the model development. In this work, we present the first iteration of QuIC-M (pronounced quick-m), an interactive human-in-the-loop data exploration and model building suite. The goal is to enable domain experts to build the machine learning pipelines an order of magnitude faster than machine learning experts while having model qualities comparable to expert solutions.

== Original document ==

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

* [http://dl.acm.org/ft_gateway.cfm?id=3209891&type=pdf http://dl.acm.org/ft_gateway.cfm?id=3209891&type=pdf]

* [http://dl.acm.org/ft_gateway.cfm?id=3209891&ftid=1966746&dwn=1 http://dl.acm.org/ft_gateway.cfm?id=3209891&ftid=1966746&dwn=1],
: [http://dx.doi.org/10.1145/3209889.3209891 http://dx.doi.org/10.1145/3209889.3209891] under the license http://www.acm.org/publications/policies/copyright_policy#Background

* [https://dblp.uni-trier.de/db/conf/sigmod/deem2018.html#BinnigBCCKSUZZ18 https://dblp.uni-trier.de/db/conf/sigmod/deem2018.html#BinnigBCCKSUZZ18],
: [https://dl.acm.org/citation.cfm?id=3209891 https://dl.acm.org/citation.cfm?id=3209891],
: [https://dl.acm.org/citation.cfm?doid=3209889.3209891 https://dl.acm.org/citation.cfm?doid=3209889.3209891],
: [http://tubiblio.ulb.tu-darmstadt.de/105228 http://tubiblio.ulb.tu-darmstadt.de/105228],
: [https://doi.org/10.1145/3209889.3209891 https://doi.org/10.1145/3209889.3209891],
: [https://academic.microsoft.com/#/detail/2804598380 https://academic.microsoft.com/#/detail/2804598380]

← Older revision	Revision as of 22:27, 1 February 2021
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Cousins et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 397855286 to Cousins et al 2018a

Scipediacontent: Created page with " == Abstract == Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data..."