A lot is written about class imbalance in machine learning (for example on this site here).
However, how to deal with "intra class" imbalance?
Assume I want to classify Bikes v.s. Cars. My training/test data is 50% about bikes and 50% cars (no class imbalance). However of this 50% cars, I have 1% Formule 1 cars, 70% small cars, 20% SUVs, 7% pick up cars and 2% jeeps.
There is no class imbalance, but what I call 'intra class' imbalance.
- Is this from a ML/statistics point of view a problem? Why is it or why is it not?
- What is the name/keyword for this problem what I call 'intra class imbalance'?