AI Article Synopsis
- A new method for validating chemometric models called a generalized Procrustes Cross-Validation is introduced, which is significantly faster and applicable to more types of models.
- This approach offers tools for analyzing data diversity, validating data splits, and identifying outliers within the datasets.
- The paper details the methodology, underlying mathematics, and shares practical results from real-world chemical data experimentation.
Article Abstract
A generalization of Procrustes Cross-Validation - recently introduced novel approach for validation of chemometric models - is proposed. The generalized approach is faster than its predecessor by several orders of magnitude and can be used for validation of a wider range of models. Furthermore, it provides new tools for exploring the heterogeneity of the dataset, quality of cross-validation splits, presence of outliers, etc. The paper describes methodological aspects of the generalized approach, based on using Procrustean rules, the mathematical background, as well as presents practical results obtained using real chemical datasets.
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| http://dx.doi.org/10.1016/j.aca.2023.341096 | DOI Listing |
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