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)
“Historians will have to face the fact that natural selection determined the evolution of cultures in the same manner as it did that of species.”
—Konrad Lorenz (1903–1989)