I have used SPSS Clementine in order to train a classifier, for this I have used a partition node with 2 parts(train and test),then using a c5-tree and cross-fold validation.
I did this because I think SPSS separates training data and does a 10-fold validation to build a best model and then test it by test data. would you please verify or criticize this?
this is the explanation of SPSS about how cross-fold works: "Cross-validate. If this option is selected, C5.0 will use a set of models built on subsets of the training data to estimate the accuracy of a model built on the full dataset. This is useful if your dataset is too small to split into traditional training and testing sets. The cross-validation models are discarded after the accuracy estimate is calculated. You can specify the number of folds, or the number of models used for cross-validation."
And if my idea is not true(to use both partitions and cross-validation) what has happened in SPSS? Is there any priority or not?