Model selection is the task of selecting 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 correct.
Read more about Model Selection: Introduction, Methods For Choosing The Set of Candidate Models, Experiments For Choosing The Set of Candidate Models, Criteria For Model Selection
Famous quotes containing the words model and/or selection:
“For an artist to marry his model is as fatal as for a gourmet to marry his cook: the one gets no sittings, and the other gets no dinners.”
—Oscar Wilde (1854–1900)
“Every writer is necessarily a critic—that is, each sentence is a skeleton accompanied by enormous activity of rejection; and each selection is governed by general principles concerning truth, force, beauty, and so on.... The critic that is in every fabulist is like the iceberg—nine-tenths of him is under water.”
—Thornton Wilder (1897–1975)