Validation and Model Application

Validation estimates whether a model generalizes beyond the samples used to fit it.

Regression Metrics

Common regression outputs include RMSEP, SEP, bias, R2, Q2, and CV predictions.

Classification Metrics

Common classification outputs include confusion matrices, accuracy, sensitivity/recall, specificity where applicable, and per-class summaries.

Split Design

Random splits are often not enough for spectra. Consider sample grouping, replicate structure, batches, time order, and instrument changes when interpreting validation results.

Applying Saved Models

When applying a saved model, confirm that the new spectra match the training contract: preprocessing, spectral axis, feature count, units, and target context. A model can produce numbers for incompatible spectra if the data shape matches but the scientific contract does not.