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Criterion for evaluating the predictive ability of nonlinear regression models without cross-validation.

Abstract：

:We propose predictive performance criteria for nonlinear regression models without cross-validation. The proposed criteria are the determination coefficient and the root-mean-square error for the midpoints between k-nearest-neighbor data points. These criteria can be used to evaluate predictive ability after the regression models are updated, whereas cross-validation cannot be performed in such a situation. The proposed method is effective and helpful in handling big data when cross-validation cannot be applied. By analyzing data from numerical simulations and quantitative structural relationships, we confirm that the proposed criteria enable the predictive ability of the nonlinear regression models to be appropriately quantified.

journal_name

J Chem Inf Model

journal_title

Journal of chemical information and modeling

authors

Kaneko H,Funatsu K

doi

10.1021/ci4003766

subject

Has Abstract

pub_date

2013-09-23 00:00:00

pages

2341-8

issue

9

eissn

1549-9596

issn

1549-960X

journal_volume

53

pub_type

杂志文章

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