How to determine which classes are easier to predict with a decision tree?

So, I'm trying to work with decision trees on Iris dataset. I've noticed by trying out different parameter (max_depth, leaves etc) that some of the classes are easier to predict (most of the trees give the same prediction). How do I justify this, and is there a way to visualize it based on different trees?