Hi Wendy. Thanks for your reply. I am not familiar with the two-stage model node. In the current process, I use the 'unbalanced' regression and test it and it seems fine (in terms of scoring there is a hierarchy - the top 10% (em_segment = 1) have a sum for sales higher than those in em_segment 2, etc...). Yet, I am convinced I can do better than that. What I tried as well is having a response model (model built on responders and not responders) and a sales model (regression only on the buyers) and trying to combine those two by applying the regression on the predicted buyers from the response model. I tested it (as before, on a more recent campaign) and in this case, it does not work. Does the two-stage model work in a somehow similar way as I just described? When applying the score created for this two-stage model, does it predict the target from the regression (sales) or the classification (response)? Since I have unbalanced data, I assume the overall process before using this node is the same as before (sampling+treatment of missing values+data partition)?? Thanks for your help.
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