PLS Introductory Overview - Training (Analysis) and Verification (Cross-Validation) Samples

A very important step when fitting models to be used for prediction of future observation is to verify (cross-validate) the results, i.e., to apply the current results to a new set of observations that was not used to compute those results (estimate the parameters). PLS offers very flexible methods for computing detailed predicted value and residual statistics for observations 1) that were not used in the computations for fitting the current model and have observed values for the dependent variables (the so-called cross-validation sample), and 2) that were not used in the computations for fitting the current model, and have missing data for the dependent variables (prediction sample; see the Observational tab on the Results dialog).