how to filter 'wrong predictions' of output of multi labeling model operator?

LeMarcLeMarc Member Posts: 72 Contributor II
edited April 2020 in Help

I usually use the Filter examples to select 'wrong predictions'.
When the operator 'multi labeling model' is used, one cant select label attributes. However those are required to select the 'wrong predictions' examples for the Filter Example Operator.
Therefore I changed the roles of the chosen attributes after applying the model & performance measure as 'label' and 'prediction' attribute in order to filter the 'wrong predictions'. However this also doesnt work.

Does anyone have an idea how to filter wrong predictions if using the multi labeling model operator?

Thank you!

Best Answers


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    LeMarcLeMarc Member Posts: 72 Contributor II
    Thanks @jacobcybulski for the remark. Im going to check it again.
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    LeMarcLeMarc Member Posts: 72 Contributor II
    @tftemme also thank you for your suggestion. I found that if I use your great idea (filter examples - wrong prediction) it works when training and testing data are from the same example set. Now I would like to use your proposal with a different data set (in terms of the values) than the example set provided for training and testing the data.

    However it does not work. If using the multi label modeling - operator , the Example Set of 'apply model' operator will only show the prediction attributes but not the original attributes. So there are no attribute at all to specify the role. Is there a solution to that?
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