[tony_bagnall]

I have taught machine learning in Java using Weka for a long time, and when we moved over to sklearn this also annoyed me. It made a good teaching point with, for example, decision trees having a dozen separate different classes for different algorithms in Weka and sklearn having one configurable one. I guess just design preference in the end. With aeon we are leaning more towards the one class per algorithm or algorithm family, but its not a hard and fast rule. One issue is when does a change in algorithm mean a change in class? So, for example, we have separate transformers for ROCKET, MINROCKET and MULTIROCKET (convolution transforms), but a single configurable RocketClassifier. UltimatelyI think it comes down to how comprehensible it is to a new user.