Definitions
from Wiktionary, Creative Commons Attribution/Share-Alike License.
- verb statistics To use a
statistical model that has too manyparameters 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
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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.
Willis E on Hansen and Model Reliability « Climate Audit 2006
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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?
Exponential Growth in Physical Systems #2 « Climate Audit 2007
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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.
More on Positive and Negative Responders « Climate Audit 2007
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I have heard someone here complain of “overfit” reconstructions.
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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?
Exponential Growth in Physical Systems #2 « Climate Audit 2007
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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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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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It doesn't take many knobs or turns of them to overfit a model when data snooping past results.
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But if you have divergence problem, it cannot be overfit at the same time
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