Description
It seems like half the bugs we've solved in the past couple of months surround problems of having different sets of classes, in the context of:
- cross-validation splitting that yields different subsets of classes in different training or testing subsets (and hence issues in alignment of class-wise outputs from
predict_proba,decision_functionor metrics, or in normalising macro-averaged scores) partial_fitwhereclassesare specified upfront, but then repeated calls need matching to those classeswarm_startwhereclasses_from the first fit must be identical to the set of classes inyin each cal 5C87 l tofit()
These are all subtly different problems, but at the moment it seems like we're handling them on an ad-hoc (and too often a post-hoc) basis.
It would be amazing if someone could review these issues and identify where either API changes (classes as a constructor parameter to classifiers has been suggested) or helper utilities might help avoid these issues in the future.