title: 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.
authors: Department of Chemical System Engineering, The University of Tokyo , 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
authors: Hiromasa
authors: H
authors: Kaneko
authors: Kimito
authors: K
authors: Funatsu
mesh_terms: D048088:Informatics / Q000379:methods*; D007928:Lethal Dose 50; D017711:Nonlinear Dynamics; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results; D012995:Solubility; D013769:Tetrahymena pyriformis / Q000187:drug effects; D013997:Time Factors; D018675:Toxicity Tests; D014867:Water / Q000737:chemistry
chemical_list: D014867:Water