multiple labels not sees as labels

wasperenwasperen Member Posts: 16 Contributor II
edited November 2018 in Help
Hi All,

I understand that a learner (for instance Neural Net) tries to learn the function to get from "regular" attributes to "label" attributes. When I change the role of an attribute from "regular" to "label" and then the next one, the first "label" just disappears. That seems to be normal behavior.

So I created a loop that assigns roles "label1", "label2", etc to my target attributes. That works well and I can see the attributes that are my functions input as "regular" and my target outputs as "labelN" (where N is the number of the label).

So far so good.

But now the Neural Network learner complains that it does not see any "label" attributes in the training set...

I have tried "label_Attribute Name" and "label Attribute Name" and "label_N" but to no avail.

Should the learner not recognize any role that starts with "label" as the target outputs?



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    landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi Willem,
    most of the learner are not capable of predicting more than one label at a time. So what you can do is to generate one model per label. You can iterate over your lables using a loop attributes operator and setting the current labelN to the role label. Then build a model on this label, and for example save it as ModelN.

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    wasperenwasperen Member Posts: 16 Contributor II
    Sounds reasonable! Thanks. I'll try it out this weekend.

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