Classification problems can exhibit a strong label imbalance in the given dataset. This can be overcome by subsampling certain class weight attributed weights, which allow for balancing the label distributions at least during model training. Stratification on the other hand will allow for keeping a certain label distribution, which stays for every respective fold.

For a regression problem this is by standard libaries e.g. scikit-learn not defined. There are few approaches to cover stratification and a well written theoretical approach for regression subsampling by Scott Lowe here.

I am wondering why label balancing for regression instead of classification problems has so few attention in the Machine Learning community? Regression problems also exhibit different characteristica that might be easier / harder acquired in a data collection setting. And then, is there any framework or paper that further addresses this issue?

Thanks in advance!


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