Running Decisions Tree model iteratively

I have a dataset of 900 rows. I want to run a decision tree model using first 0-50 rows for test set and remaninig 850 for train. For every iteration test set changes to next 50 rows (ie 50-100) and remaining as training and repeat this until test set equals 850-900 and remainnig as training,

Can this be achieved with KNIME worflow controls ? I tried using row splitters with loops but couldn't get the right workflow.

Thanks !

Mohammed Ayub


Hi Mohammed,

you can use the X-Validation nodes for this. Please see the following example . Inside the meta node (open with double click) you have a x-partitioner node, this you need to set to Linear Sampling and 900/50 = 18 validations.

Cheers, Iris