apply multi label modeling - How to keep original attributes in a data set
below is a model I build with training data (1) by using the multi label modeling operator. That particular model is applied to another data set (2). Number of attributes and Attribute names are the same in both data set. Only the values of the attributes differ. The goal is to filter wrong predictions & filter other examples based on customized filtering. For the latter one, original attributes of data set (2) are required.
However in the result example set of (2) the original attributes disappear and only the predictions are shown. If I set the original attributes as label (or any other roles) - only "one" original attribute will be shown in the example set of (2). And by doing this the multi label modeling performance - operator does not work since no labels are allowed anyway.
How can I keep the original attributes of data set (2)?
The filter examples operator within the multi label performance operator also does not work if I use the data set (2) to apply the trained model on (s. below). Theres an error note which says "attribute xx does not exist)". But if the model is trained on the data set (1) and also applied on the same date set (1) - wrong predictions can be filtered.
How can I filter wrong predictions of the data set (2)?
Thank you for the help!