Figure 1.

The 10-fold cross validation for analysis of test data. Twenty rhombuses are randomly partitioned into 10 subsets, with two rhombuses in each subset. Of the 10 subsets, one subset is retained as the validation data, and the remaining nine subsets are used to train the model. This cross-validation process is then repeated 10 times. In summary, cross-validation combines measures of 10 fitness and provide an average.