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Normally when evaluating a model, the training data is split into a test set and a training set.

I want to evaluate the best possible performance of the classifier on my task.

So I have Trained it on all my data, and tested it on all my data. The results are not all 100% for all variation on the model I am investigating, which is worth reporting on.

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  • $\begingroup$ You may have to provide more detail on how you developed your model (or whether it was a pre-existing model, for which you estimated the parameters on the training set) and what you did to avoid overfitting. Some approaches have well recognized names (e.g. leave-one-out cross-validation, x-fold cross-validation etc.). $\endgroup$
    – Björn
    Aug 26, 2015 at 12:04
  • $\begingroup$ I am also doing various forms of cross-validation. But not here. Here I want the classifier to overfit. The purpose of setting the test and training set to be the same is to assess if the classifier has the power to apply to this data at all. $\endgroup$ Aug 26, 2015 at 12:08
  • $\begingroup$ If your main approach to avoid overfitting is cross-validation, then I guess people would know what you did, if you wrote e.g. "naively applying the classifier without cross-validation". $\endgroup$
    – Björn
    Aug 26, 2015 at 14:57

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Since you mention you're doing that as a comparison for methods of cross-validation, it can be called "not doing cross-validation". One can also say that you don't have test set (unseen data) at all, only training set.

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