This node is the first in a cross validation loop. At the end of the loop there must be a X-Aggregator to collect the results from each iteration. All nodes in between these two node are executed as many times as iterations should be performed.


Number of validations
The number of cross validation iterations that should be performed.
Linear sampling
If checked, the input table is cut into consecutive pieces.
Random sampling
If checked, the partitions are sampled randomly from the input table.
Stratified sampling
If checked, the partitions are sampled randomly but the class distribution from the column selected below is maintained.
Random seed
For random and stratified sampling you can choose a seed for the random number generator in order to get reproducible results. Otherwise you get different partitions every time.
Class column name
The name of the column with the class labels.
Performs a leave-one-out cross validation, i.e. there are as many iterations as data points and in each iteration another point's target value is predicted by using all remaining points as training set.

Input Ports

The datatable that is to be split

Output Ports

The data table with the training data
The data table with the test data


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