Cited by 2,300Statistical Methods and InferenceMachine Learning and Data ClassificationStatistical Methods and Applications

Estimating the error rate of a prediction rule: improvement on cross-validation

Efron, Bradley·1983·Journal of the American Statistical Association

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.

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Crossref · 10.1080/01621459.1983.10477973

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Authors
Efron, B.
Affiliations
Stanford University (US)
Corresponding author
Efron, Bradley
Venue
Journal of the American Statistical Association
Published
1983-06
Volume
78
Issue
382
Pages
316-331
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W2040615655
Cited by
2,300
References
16
Citations by year
2026: 40 · 2025: 45 · 2024: 44 · 2023: 57 · 2022: 87 · 2021: 77 · 2020: 92 · 2019: 100 · 2018: 99 · 2017: 81 · 2016: 77 · 2015: 103 · 2014: 94 · 2013: 100 · 2012: 104
FWCI
19.32
Type
journal article
Language
en
Open access
closed
Locations
Journal of the American Statistical Association (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
error rate estimation, prediction rules, cross-validation, bootstrapping, classification, small sample estimation, estimation bias, generalization error

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