Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation

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• 作者：Hiromasa Kaneko ; Kimito Funatsu
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：September 23, 2013
• 年：2013
• 卷：53
• 期：9
• 页码：2341-2348
• 全文大小：344K
• 年卷期：v.53,no.9(September 23, 2013)
• ISSN：1549-960X

文摘

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.