Let's imagine I have two completely different multi-class ML models, let's call them ML1 and ML2. The models were trained on completely different data with different target classes. As an output, the models print the distributions over the target classes. For example, something like this
ML1: [0.1, 0.2, 0.7] ML2: [0.1, 0.1, 0.3, 0.4, 0.1]
According to the output, ML1 decides that it's input instance belongs to the 3rd class and ML2 decides that it's input instance belongs to the 4rd.
What I want to do is to decide which model is surer about it's prediction. Apparently I cannot directly compare the models output. Somehow I should normalize the outputs. I would appreciate any help.