Validation of chemometric models - a tutorial
Frank Westad1, Federico Marini2
1CAMO Software AS, Nedre Vollgate 8, N-0158 Oslo, Norway.
Analytica Chimica Acta
|September 24, 2015
Summary
Careful dataset validation is crucial for reliable model performance. Random splitting requires caution, especially when data stratification may bias results like Root Mean Square Error (RMSE) or R-squared (R²).
Area of Science:
- Chemometrics
- Data Science
- Statistical Modeling
Background:
- Random dataset splitting is common but requires careful application.
- Systematic stratification can bias validation metrics (e.g., RMSE, R²).
- Understanding validation levels (repeatability, reproducibility, variation) is essential.
Purpose of the Study:
- To provide a comprehensive guide to numerical and conceptual validation.
- To highlight potential pitfalls in common validation procedures.
- To emphasize the importance of model robustness for future predictions.
Main Methods:
- Discussing the careful application of random calibration/test set splitting.
- Illustrating validation across different levels: repeatability, reproducibility, and material/instrument variation.
- Examining model robustness and its impact on predicting future samples.
Main Results:
- Demonstrating how validation strategies affect figures of merit (RMSE, R²) and model dimensionality.
- Showing the critical importance of robust models for reliable future predictions.
- Highlighting the need for consensus on significant variables across methods and literature.
Conclusions:
- Validation requires careful consideration of data structure and potential biases.
- Robust model validation is key for accurate predictions and reliable scientific conclusions.
- Cross-validation with literature and chemical knowledge ensures model generalizability.
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