A comparison of estimators for the variance of cross–validation estimators of the generalization error of computer algorithms

Marianthi Markatou; Rositsa Dimova; Anshu Sinha

doi:10.1142/9789814340564_0014

A comparison of estimators for the variance of cross–validation estimators of the generalization error of computer algorithms

Marianthi Markatou
, Rositsa Dimova
, Anshu Sinha

Department of Biostatistics

Columbia University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

This paper studies variance estimators of cross-validation estimators of the generalization error. Three estimators are discussed, and their performance is evaluated in a variety of data models and data sizes. It is shown that the standard error associated with the moment approximation estimator is smaller than that associated with the other two. The effect of training and test set size on these estimators is discussed.

Original language	English
Title of host publication	Nonparametric Statistics and Mixture Models
Subtitle of host publication	A Festschrift in Honor of Thomas P Hettmansperger
Publisher	World Scientific Publishing Co.
Pages	226-251
Number of pages	26
ISBN (Electronic)	9789814340564
ISBN (Print)	9814340553, 9789814340557
DOIs	https://doi.org/10.1142/9789814340564_0014
State	Published - Jan 1 2011

Keywords

Cross-Validation
Generalization error
Moment approximation
Variance

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10.1142/9789814340564_0014

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A comparison of estimators for the variance of cross–validation estimators of the generalization error of computer algorithms. / Markatou, Marianthi; Dimova, Rositsa; Sinha, Anshu.
Nonparametric Statistics and Mixture Models: A Festschrift in Honor of Thomas P Hettmansperger. World Scientific Publishing Co., 2011. p. 226-251.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AB - This paper studies variance estimators of cross-validation estimators of the generalization error. Three estimators are discussed, and their performance is evaluated in a variety of data models and data sizes. It is shown that the standard error associated with the moment approximation estimator is smaller than that associated with the other two. The effect of training and test set size on these estimators is discussed.

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KW - Generalization error

KW - Moment approximation

KW - Variance

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A comparison of estimators for the variance of cross–validation estimators of the generalization error of computer algorithms

Abstract

Keywords

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