Question on the ML side of this post: How are these "parameterizations" used? Is this really just feature engineering with a new name? Are they including this information when training the model?
In the article, they mention using the new labels to build a "more balanced" dataset -- is this a realistic possibility in practice when most teams still have a dearth of data?
In the article, they mention using the new labels to build a "more balanced" dataset -- is this a realistic possibility in practice when most teams still have a dearth of data?