  While coresets have been growing in terms of their application, barring few
exceptions, they have mostly been limited to unsupervised settings. We consider
supervised classification problems, and non-decomposable evaluation measures in
such settings. We show that stratified uniform sampling based coresets have
excellent empirical performance that are backed by theoretical guarantees too.
We focus on the F1 score and Matthews Correlation Coefficient, two widely used
non-decomposable objective functions that are nontrivial to optimize for and
show that uniform coresets attain a lower bound for coreset size, and have good
empirical performance, comparable with ``smarter'' coreset construction
strategies.
