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Validation and data splitting in predictive regression modeling of honing surface roughness data

机译：珩磨表面粗糙度数据的预测回归建模中的验证和数据分割

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摘要

Model validation is critical in predicting the performance of manufacturing processes. In predictive regression, proper selection of variables helps minimize the model mismatch error, proper selection of models helps reduce the model estimation error, and proper validation of models helps minimize the model prediction error. In this paper, the literature is briefly reviewed and a rigorous procedure is proposed for evaluating the validation and data splitting methods in predictive regression modeling. Experimental data from a honing surface roughness study will be used to illustrate the methodology. In particular, the individual versus average data splitting methods as well as the fivefold versus threefold cross-validation methods are compared. This paper shows that statistical tests and prediction errors evaluation are important in subset selection and cross-validation of predictive regression models. No statistical differences were found between the fivefold and the threefold cross-validation methods, and between use of the individual and average data splitting methods in predictive regression modeling.
机译：模型验证对于预测制造过程的性能至关重要。在预测回归中，正确选择变量有助于最小化模型失配误差，正确选择模型有助于减少模型估计误差，而正确验证模型则有助于最小化模型预测误差。在本文中，简要回顾了文献，并提出了一种严格的程序来评估预测回归建模中的验证和数据拆分方法。来自珩磨表面粗糙度研究的实验数据将用于说明该方法。特别是，比较了个人数据对比平均数据拆分方法以及五重对比三重交叉验证方法。本文表明，统计测试和预测误差评估在预测回归模型的子集选择和交叉验证中很重要。在五重和三重交叉验证方法之间，以及在预测回归建模中使用个体数据拆分方法和平均数据拆分方法之间，未发现统计差异。

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