Tree to Rules and Classification Error
The tree inside "Tree to Rules" is the copy-and-paste of "Tree W".
Both the operators, Tree W and Tree to Rules, are passed to the same Apply Model and Performance operators (just the names are different, they are copy-and-paste, so the same configurations).
How is it possible that the Classification Error of Tree W is 5.59% and that of Tree to Rules is 0.79%?
They have the same input, they go through the same performance test and Tree W is also executed inside Tree to Rules.
The only difference, visually speaking, is that Tree to Rules is kind of overfitting generating tons of rules while Tree W don't.
How is it possible?
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Are you training and testing applying the model on the same data? looks like you are doing that. Is it possible that you provide your dataset and process (Export to .rmp file using File --> Export Process) in this thread for us to check.
I tried based on your requirement and it gave me the same outputs.