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Thread: WEKA using class values to solve decision tree?

  1. #1
    Join Date
    Sep 2016
    Posts
    1

    Default WEKA using class values to solve decision tree?

    I am brand new to WEKA and ML, so please excuse my ignorance with the following. I've wasted several hours trying to figure it out, so hopefully someone could point me in the right direction:
    I am trying to run a J48 decision tree on data for USDJPY. The data was loaded via .csv file and the class value is of nominal type, more specifically a value of TRUE or FALSE if USDJPY was trading more than 1% higher after 20 sessions. The problem is, When I run the algorithm, the decision tree is simply using the class value to solve the problem, which is useless. There are 7 attributes other than the class attribute from which I am looking to predict the class attribute.
    When comparing my dataset to the example "glass" dataset, I cannot find any difference between the two that would explain my problem. "glass.arff" works as expected when I run J48 (with identical settings) by trying to predict the class value (type of glass) via the other attributes (ie it gets some guesses wrong).
    What am I missing here? Perhaps it has something to do the .csv source file type?
    Last edited by tsferro; 09-17-2016 at 03:01 PM.

  2. #2
    Join Date
    Aug 2006
    Posts
    1,741

    Default

    If you mean that J48 does not build a tree on your data (i.e. it just contains the root node predicting the majority class value from the training data) then this means that the algorithm could not find any useful relationship between your input attributes and the class. Probably it has pruned any learned tree structure back to the root. You could try turing off the pruning mechanism, but I'd say it is unlikely that the performance would be any better than just predicting the majority class.

    Cheers,
    Mark.

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