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Subject Item
n2:
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
n14:
n9:wikiPageUsesTemplate
n10: n11:
n5:
31
n4:
May
n6:
Notability, Verifiability, orphan
n3:
190.0
n15:
20100531031020
n12:
2010
n8: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.