I'm quite new with KNIME. My questions is: I ran a decision tree with a credit dataset. I've built a project with the classical nodes (file reader, partitioning , decisision tree, scorer) and executed. The decision variable is a nominal one with the values "good" or "bad" according to the credit check results.
How can run the same classification tecnique on a new dataset where I do not have the "good" / "bad" variable set. In other words how can I apply the model to new data?
Have you tried running the predictor without it and got an error?
You don't need to target column when predicting, just connect the Decision Tree Predictor node to the output of the Decision Tree Learner and the data input only needs to have the descriptor columns, you don't need the target column.