Definitions
from Wiktionary, Creative Commons Attribution/Share-Alike License
- v. To use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
Etymologies
from Wiktionary, Creative Commons Attribution/Share-Alike License
Examples
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This is, in some ways, the worst kind of overfit because the selection process is unpenalized re: the number of candidates tested. i.e.
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You can check whether it is overfit or no: divide data to two parts, repeat the procedure and verify that the order of most correlated series does not change.
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It doesn't take many knobs or turns of them to overfit a model when data snooping past results.
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Next, if they are structually unstable then what would this mean for the overfit parameters that are derived through the trial-and-error process that you describe basically, a genetic algorithm?
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If the GCMs work well for an idealized parameter say, GMT because they are overfit to that parameter, then the lack of fit to some other equally meaningful parameter would put them under suspicion.
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But if you have divergence problem, it cannot be overfit at the same time
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I have heard someone here complain of “overfit” reconstructions.
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If one stands in the shoes of the people doing reconstructions with changing temperature series, they should either be, at least, somewhat concerned that these results indicate that the reconstructions were overfit or, if not, they must be very concerned about the legitimacy of the temperature changes and be publicizing that point.
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The uncertainty is in regard to the degree of accuracy of the reconstructions which ARE based on overfit models.
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If one views the reconstructions as overfit, then the discrepancies say nothing about the legitimacy of any temperature series used.
Comments
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