Cross-validation calculates the predictive ability of potential models to help you determine the appropriate number of components to retain in your model. Use cross-validation to determine the optimal number of components for your data. If the data contain multiple response variables, Minitab validates the components for all responses simultaneously. For more information, go to Cross-validation in PLS regression.
Minitab can perform three different cross-validation methods:
- None: Do not perform cross-validation.
- Leave-one-out: Use this option to calculate potential models leaving out one observation at a time. For large data sets, this method can be time-consuming, because it recalculates the models as many times as there are observations.
- Leave-group-out of size: Enter the number of observations that will be excluded each time the model is recalculated. Because this method reduces the number of times it has to recalculate a model, it is most appropriate when you have a large data set.
- Leave out as specified in column: Use this option to calculate the models by simultaneously excluding observations that have matching numbers in the group identifier column. This method allows you to specify which observations are omitted together. For example, if the group identifier column includes numbers 1, 2, and 3, all observations with 1 are omitted together and the model is recalculated. Next, all observations with 2 are omitted and the model is recalculated, and so on.