About: Treatment learning   Sponge Permalink

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Treatment learning is a process by which an ordered classified data set can be evaluated as part of a data mining session to produce a representative data model. The data model should describe some key property of the data set. The output of a treatment learning session is a treatment, a conjunction of attribute-value pairs. The size of the treatment is the number of pairs that compose the treatment. From: Three concepts can be used to define treatment learning: lift, minimum best support, and treatment effect size.

AttributesValues
rdfs:label
  • Treatment learning
rdfs:comment
  • Treatment learning is a process by which an ordered classified data set can be evaluated as part of a data mining session to produce a representative data model. The data model should describe some key property of the data set. The output of a treatment learning session is a treatment, a conjunction of attribute-value pairs. The size of the treatment is the number of pairs that compose the treatment. From: Three concepts can be used to define treatment learning: lift, minimum best support, and treatment effect size.
dcterms:subject
dbkwik:annex/prope...iPageUsesTemplate
Day
  • 31(xsd:integer)
Month
  • May
concern
  • Notability, Verifiability, orphan
Time
  • 190.0
Timestamp
  • 20100531031020(xsd:double)
Year
  • 2010(xsd:integer)
abstract
  • Treatment learning is a process by which an ordered classified data set can be evaluated as part of a data mining session to produce a representative data model. The data model should describe some key property of the data set. The output of a treatment learning session is a treatment, a conjunction of attribute-value pairs. The size of the treatment is the number of pairs that compose the treatment. From: Three concepts can be used to define treatment learning: lift, minimum best support, and treatment effect size.
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