How to measure class imbalance with a single number I am comparing classification algorithms on different data-sets. For some of the algorithms, there are reasons to believe they don't work well with class imbalance unless some kind of adjustment is made. If the adjustments work well, the experiments on the imbalanced sets should show that the adjustment helps and the balanced sets will tell me if the adjustment remains at least neutral (not harmful) where it is not really needed.
I would like to introduce the used data-sets in a table with columns for the number of records, variables and classes and also one column that measures the class imbalance.
Potential measures that came to my mind where the ratio between the prevalence of the most and the least common class. I also thought about using something like the Herfindahl index. It does not correct for the number of classes, which in economics it shouldn't, but I would need it to do.
Do you have any suggestions? I would like to avoid listing the prevalences of all classes for all data-sets since the number of classes differs.
 A: 
Potential measures that came to my mind where the ratio between the prevalence of the most and the least common class.

I think this is the right idea. Actually, I would suggest that you use two numbers instead of just one, and simply present the prevalence of the most and least common classes. This is direct and easy to understand.
A: Yes, I support that answer.  Simply represent imbalance as the percent in the minority class.  It's simple and everyone understands it.  Do not represent it as a ratio-- this gets highly non-linear and doesn't generalize to multi-class problems.
A perfectly balanced binary-class dataset would be 50%.   If we have 100 classes that are perfectly balanced, we'd expect 1% for the minority class, which is already hard; but if the minority class in this case were 0.001% we know it's even more imbalanced; it can be useful to report the number of train/test examples in the minority class as well.
If you can/want to report more than a single scalar, report all the percentages, sorted; (or summarize them as the min and median across the classes).  This lets us differentiate between one rare class, and several rare classes.
