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I have tried converting all the files (training, labeled test data and un-labeled test data) prefereably in arff format and it is working good for my labelled test data. But for unlabelled test data it keep poping the error (files are not compatible or different number of labels).

Here is what I tried: I added "?" for missing values in the "class label" column of un-labelled test data and also converted the type of "class label" attribute from string to nominal using weka filters (because intitially the error was "str not compatible to nom"). However, it is still not working to make predictions of un-labelled test data and still giving the error (different nummber of labels). In my opinion I am doing it in a wrong way, Can someone tell me where am I wrong or any suggestion to do it in correct way? Thank you very much

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  • Are the attribute definitions for the class attribute in the @header section of your train/unlabeled ARFF files the same? If not, then this is most likely why Weka complains about it.
    – fracpete
    Commented Jun 24 at 21:42
  • Yes they are same. I copied the header of training data set to the un-labeled test data. Commented Jun 25 at 11:05
  • If they have the exact same header, then Weka wouldn't complain about differing number of labels. Can you post the two headers?
    – fracpete
    Commented Jun 25 at 22:18
  • sure, I can post it for you. Commented Jun 26 at 11:15
  • Hi fracpete, my headers were not posted, it seems there is a limit of words in the comment section. Commented Jun 29 at 13:19

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