When using repeated cross-validation for evaluation, should I aggregate the results for each cross-validation?

When aggregating, a 10-times repeated 10-fold, will provide 10 values for each measure like AUC. (Therefore, k=10 for my t-test with k-1 degrees of freedom when comparing algorithms.)

When not-aggregating, a 10-times repeated 10-fold will provide 100 values, as each fold is considered as a single run.

WEKA does not aggregate. However, Han writes in his data mining book (p370) that each CV should be aggregated.

I personally am a fan of not-aggregating: a 10 x 10-fold CV produces 100 models, so one should use 100 results for comparison. Some of my colleagues disagree, though.

Addition: I am doing the repeated CV for:

  • Comparing two algorithms with a paired t-test

  • Getting a variance estimate for my model performance

  • $\begingroup$ Practically how do you compare 100 numbers? The brains fits 7 at a time in any case. You'll end up aggregating in the end one way or another, explicitly or implictly. It's better to do explicitly $\endgroup$
    – Aksakal
    Oct 13, 2016 at 15:15

1 Answer 1


As mentioned above WEKA uses the 100 results of the single folds as input for the (corrected resampled) t-test.

According to a mailing list post, aggregating the results for each cross-validation run would lead to an increased Type I error rate (i.e., not existing significant differences are detected between algorithms).

Hence, each repetition of the cross-validation should NOT be aggregated before the t-test.


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