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An R-Based Landscape Validation of a Competing Risk Model

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Procrustes Cross-Validation-A Bridge between Cross-Validation and Independent Validation Sets.

Sergey Kucheryavskiy¹, Sergei Zhilin², Oxana Rodionova³

¹Department of Chemistry and Bioscience, Aalborg University, Niels Bohrs vej 8, Esbjerg 6700, Denmark.

Analytical Chemistry

|August 14, 2020

Summary

This study introduces a novel pseudo-validation set for chemometric models, enhancing validation beyond standard k-fold cross-validation. This method provides a more robust assessment of model performance using sampling uncertainty.

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Area of Science:

Chemometrics
Data Science
Analytical Chemistry

Background:

Chemometric model validation is crucial for reliable predictions.
Conventional k-fold cross-validation has limitations in assessing sampling uncertainty.
Independent test sets are ideal but often unavailable.

Purpose of the Study:

To propose a new approach for validating chemometric models.
To introduce a pseudo-validation set that quantifies sampling uncertainty.
To enable more comprehensive model evaluation beyond traditional methods.

Main Methods:

Development of a novel approach based on k-fold cross-validation.
Creation of a pseudo-validation set incorporating sampling uncertainty.
Application to simulated and real chemical datasets.

Main Results:

The pseudo-validation set allows computation of metrics not available in conventional cross-validation.
Experimental results demonstrate the utility of the approach with both simulated and real data.
The method effectively estimates sampling uncertainty for improved model validation.

Conclusions:

The proposed pseudo-validation set offers a valuable alternative for chemometric model validation.
This approach enhances the reliability and interpretability of model performance assessment.
It provides a practical solution when independent test sets are not feasible.