Analyze model Leave-One-Out error for <output>
Model menu > Error (Leave-One-Out)> Error over Training Data Size
First, you have to specify the quantity for the investigation to start (Start Training Size), the quantity for the investigation to end (End Training Size) the interval to the next data size (Step Width) and the 
number of subsets (Number of Repetitions) used for the calculation of the error.
After you click OK, <Number of Repetitions> different subsets of the training data record are selected for the analysis, and the leave-one-out error is determined in each case. The bar shows the variance of these <Number of Repetitions> results, the solid line the mean value of the results.
This allows identifying whether the model improves if more training data are used or if the size of the training data can even be reduced since no appreciable model improvement can be achieved starting at a certain size.
The larger the subsets, the more time is required for the calculation!
An Analyze model Leave-One-Out error for <output> window opens for each output. Those windows show the average model error (RMSE) for each output depending on the number of training data used.
The Analyze model Leave-One-Out error for <output> window contains the following elements:
