@MBRACH - Thank you for your comment. @MelodieRush was busy so she asked me to respond to your note. There is likely not a problem in this situation. The Metadata node is only able to display the metadata for one preceding node at a time, so you should not be looking at the Metadata node to see the differences in the models that are formed. Instead, consider creating multiple Metadata nodes with different settings for the Combine property (Combine = Any, Combine = All, Combine = Majority) and then follow each of those Metadata nodes with a default Regression node where no additional variable selection is done. You can then connect all of the new Regression nodes to a new Model Comparison node and run the flow. You will then likely see differences in the Regression node results as well as in the Model Comparison node. Depending on complexity of the relationships in your data, it is possible that not all of the fitted models will be different. Also, given the large number of variable selection methods you are using, it is possible that using Combine=Any will result in no input variables being selected at all since this option rejects a variable if any of the variable selection methods rejected that variable. You might also consider looking at a subset of variables selection methods rather than so many for Combine=Any. I often like to use Combine=All and do further variable selection in the final modeling node when possible. For example, using a selection method in a final Regression node so that it chooses only the variables that are necessary for that model. If your final modeling node does not have variable selection (e.g. a Neural Network node), then you might consider using different subsets of variables and passing them to different Neural Network nodes to see how they impact the overall fit.
Hope this helps!
Cordially,
Doug
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