  Machine learning practitioners often compare the results of different
classifiers to help select, diagnose and tune models. We present Boxer, a
system to enable such comparison. Our system facilitates interactive
exploration of the experimental results obtained by applying multiple
classifiers to a common set of model inputs. The approach focuses on allowing
the user to identify interesting subsets of training and testing instances and
comparing performance of the classifiers on these subsets. The system couples
standard visual designs with set algebra interactions and comparative elements.
This allows the user to compose and coordinate views to specify subsets and
assess classifier performance on them. The flexibility of these compositions
allow the user to address a wide range of scenarios in developing and assessing
classifiers. We demonstrate Boxer in use cases including model selection,
tuning, fairness assessment, and data quality diagnosis.
