Abstract
Abstract We construct a prediction rule on the basis of some data, and then wish to estimate the error rate of this rule in classifying future observations. Cross-validation provides a nearly unbiased estimate, using only the original data. Cross-validation turns out to be related closely to the bootstrap estimate of the error rate. This article has two purposes: to understand better the theoretical basis of the prediction problem, and to investigate some related estimators, which seem to offer considerably improved estimation in small samples.
Keywords
EstimatorCross-validationBasis (linear algebra)Computer scienceStatisticsMean squared prediction errorConstruct (python library)Word error rateEconometricsData miningMathematicsArtificial intelligence
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Publication Info
- Year
- 1983
- Type
- article
- Volume
- 78
- Issue
- 382
- Pages
- 316-331
- Citations
- 2126
- Access
- Closed
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Cite This
Bradley Efron
(1983).
Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation.
Journal of the American Statistical Association
, 78
(382)
, 316-331.
https://doi.org/10.1080/01621459.1983.10477973
Identifiers
- DOI
- 10.1080/01621459.1983.10477973