I am applying a decision tree learner. Before that, I apply Normalizer to normalize the data, and partition it in two partitions. Finally, I am applying the learned tree on the predictor, with the second partition as entry.
When I am analyzing my decision tree, the values of the decision points are also normalized. This is not useful, because I would like to see the non-normalized values displayed to interpret the tree. Is there a way in Knime to achieve this, i.e. on the basis of learned normalized decisions tree to have a decision tree that shows the non-normalized values?
Thanks for the help