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| 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.
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| dbkwik:annex/property/wikiPageUsesTemplate | |
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| concern | - Notability, Verifiability, orphan
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| 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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