Model selection
Task of selecting a statistical model from a set of candidate models
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one.
In the context of machine learning and more generally statistical analysis, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice.
This brief starts where responsible research should: with the source description of “Model selection” as task of selecting a statistical model from a set of candidate models. Everything that follows is an evidence route, not borrowed authority.
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The subject matters to the general reference register because the source frames it as task of selecting a statistical model from a set of candidate models. Its deeper value depends on whether names, dates, institutions and citations support that framing.
Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Sep 2, 2026.
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This entry incorporates text from “Model selection” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.