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Thread: Predictions on unlabeled ("scoring" / "test") data in Java Weka

  1. #1
    Join Date
    Aug 2016

    Question Predictions on unlabeled ("scoring" / "test") data in Java Weka

    I'm using Java to train various tree model classifiers on data from .csv and SQL databases.

    I am able to train and cross-validate these models without issue. However, I am unable to generate predictions on the unlabeled new data, which I believe is because the new data naturally has 1 less column than the training data (i.e. the outcome variable is not in the new data).

    Is the only way to address this problem to add an empty column with the name of the outcome variable in the new data?

    I appreciate any confirmation / information on this; it seems to be a very unusual requirement to have the outcome variable in a prediction data set, but if that's what I need to do then so be it. I'll need to figure out how to add that column in Java, but please reply even if you're not able to help me do that.


  2. #2
    Join Date
    Aug 2006


    Yes, you are correct - the outcome variable needs to be declared in the test data. However, all values can be set to missing (i.e. ?). Weka's Add filter is a convenient way to add such a column, as it sets all values to missing for the new attribute.


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