I have a binary classification problem where the penalty incurred for a wrong prediction varies (basically linearly) with the value of one of the continuous features. The higher this feature, the higher the penalty for a wrong prediction
Is there a common way to bake this into a model? I’m thinking this requires a custom loss function but am not sure where to begin research. Is there a buzzword I should search, an easier way to include this idea, or academic papers that explore this?
I’ve sort of hit a wall in my searching. Thanks!