I'm working on a problem, in which the learning algorithm consistently produces high accuracy on the different samples it is tested on, but generally has a rather unsatisfactory adjusted Rand index (that is, negative or close to 0).
However, I'm unable to find a valid explanation to this. That is, the model is predicting with high accuracy, but it is still worse than random assignment in general? I doesn't seem to add up to me.
I apologise for not being able to provide more detail on what I'm working on, but I hope this would be enough to open a discussion around this topic.