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weka

methods does not give confusion matrix in weka

I want to do classification in weka. I am using some methods(Random Tree, Random Forest, Decision Table, RandomSubspace...) but they give results like below. 

=== Cross-validation ===
=== Summary ===

Correlation coefficient                  0.1678
Mean absolute error                      0.4832
Root mean squared error                  0.4931
Relative absolute error                 96.6501 %
Root relative squared error             98.6323 %
Total Number of Instances           100000

However I want results as accurancy and confusion matrix. How can I get results like that?

Note: When I use small dataset, it gives results as confusion matrix. Can it be related with the size of dataset?

Solution

  • The output of the training/testing in Weka depends on the type of the attribute that you are trying to predict. If your attribute is nominal, you will get a confusion matrix and accuracy value. If your attribute is numeric, you will get a correlation coefficient.

    In your small and large datasets that you mention, what is your type of the attribute that you are predicting?