Could anyone suggest best practices within the tidymodels ecosystem for building multi-class models where a one-vs-rest (A vs not A, B vs not B, C vs not C, ... etc.) approach was used?
I never start with that approach, but occasionally I will build a multi-class classifier which, despite attempts at tuning, up/down sampling, feature engineering, etc., really struggles to predict a particular class.
For example, if a multinomial logit did a poor job at predicting class "A", one might think of building an A vs not A binary logistic regression with a relatively high recall % and attempting to blend that probability with the probability of predicting "A" in the multinomial model.
Is this a bad idea in general? If this is not a bad idea, does anyone have any suggestions on best practices for doing such a thing within tidymodels?