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It is the averaged results of the iterations that is used in the report of the cross validation results.Leave-One-Out: Each single object in the data set is used as a test set.An additional plot that is made available by including cross-validation in the Analysis GUI is the analog to the common "calibration curve" scatter plot of predicted versus actual Y values.
Contiguous Blocks: Each test sets is determined by selecting contiguous blocks of n/s objects in the data set, starting at object number 1.
If this checkmark is red, this indicates that your most recently specified cross-validation settings will be applied to the next analysis of the loaded data. Consequently, once the Analysis is executed, cross-validation results and plots will be made available along with results and plots for the "full" model.
For example, the Model Results window in Analysis will include a Root Mean Square Error of Cross-Validation (RMSECV) value along with the Root Mean Square Error of Calibration (RMSEC) value.
A typical cross-validation procedure usually involves more than one sub-validation experiment, each of which involves the selection of different subsets of samples for model building and model testing.
As there are several different modeling methods in chemometrics, there are also several different cross-validation methods, and these vary with respect to how the different sample subsets are selected for these sub-validation experiments.
When using the Analysis GUI, the cross-validation settings can be accessed by clicking on the red checkmark on the corner of the Model button of the status panel; or by selecting the "Choose Cross-Validation" button on the Analysis Flowchart, or by selecting the Tools menu on the Analysis GUI, then selecting Cross-Validation.