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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

Hiromasa Kaneko¹, Kimito Funatsu

¹Department of Chemical System Engineering, The University of Tokyo , 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Journal of Chemical Information and Modeling

|August 27, 2013

Summary

New performance criteria for nonlinear regression models eliminate the need for cross-validation. These metrics effectively evaluate predictive ability, especially for big data applications.

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Area of Science:

Statistics
Computational Chemistry
Data Science

Background:

Cross-validation is a standard technique for evaluating predictive models.
However, cross-validation is computationally intensive and often infeasible for large datasets.
Updating regression models necessitates alternative methods for performance evaluation.

Purpose of the Study:

To propose novel predictive performance criteria for nonlinear regression models.
To offer an alternative to cross-validation, particularly for updated models and big data.
To enable robust evaluation of model predictive capabilities.

Main Methods:

Development of new performance criteria: determination coefficient and root-mean-square error.
Application of criteria to midpoints between k-nearest-neighbor data points.
Validation using numerical simulations and quantitative structure-activity relationship (QSAR) data.

Main Results:

The proposed criteria effectively quantify the predictive ability of nonlinear regression models.
These criteria are applicable even after models have been updated.
The method demonstrates efficacy in big data scenarios where cross-validation is impractical.

Conclusions:

The proposed performance criteria offer a viable alternative to cross-validation.
These metrics are particularly valuable for evaluating updated nonlinear regression models.
The approach facilitates accurate assessment of predictive performance in big data contexts.