What's the correct way to calculate sample weight in a multi-task model?
Concretely, I have a model that outputs a 400 class [multi-class] classification, as well as a 5 class multi-label classification. The classes are all disproportional and out of balance. Without any sample weighting, the multi-class classifier gets ~90% top 3 accuracy, and good AUC on some of classes in the multi-label classifications. The goal is to improve performance in one of the classes in the multi-label classification task. This class is fairly unbalanced.
What I'm attempting is to set the sample weights to a fixed value (say, 0.5) when the class of interest is not set and some other arbitrary fixed value (say, 2.5) when it is.