Dealing with very small and unbalanced data I am working on some TV series data, so the number of records is very limited. I have 58 instances, one for each existing episode, which I have randomly split in 45 and 13. The main goal is to make a classification over  five classes.
In order to make everything even worse, the data was unbalanced:
Feature    Dim
---------+-----
Class 1  | 24
Class 2  | 15
Class 3  |  7
Class 4  |  7
Class 5  |  5

This is why I applied SMOTE to the training data, but this keeps me with the problem of having a very small testing dataset. In fact, this is how the test data is distributed:
Feature    Dim
---------+-----
Class 1  |  5
Class 2  |  3
Class 3  |  2
Class 4  |  2
Class 5  |  1

How can I measure performance on such little data? I can't tell how many "Class 5" elements it is able to recognize, because there is just one element.
Is there a way to handle this?

Train/test split sounds like a bad idea indeed. I'd try jackknife resampling/LeaveOneOut cross-validation for this case.
