  Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to
both establish an appropriate level of trust in machine learning models and
identify future model improvements. In this paper we demonstrate CrossCheck, an
interactive visualization tool for rapid crossmodel comparison and reproducible
error analysis. We describe the tool and discuss design and implementation
details. We then present three use cases (named entity recognition, reading
comprehension, and clickbait detection) that show the benefits of using the
tool for model evaluation. CrossCheck allows data scientists to make informed
decisions to choose between multiple models, identify when the models are
correct and for which examples, investigate whether the models are making the
same mistakes as humans, evaluate models' generalizability and highlight
models' limitations, strengths and weaknesses. Furthermore, CrossCheck is
implemented as a Jupyter widget, which allows rapid and convenient integration
into data scientists' model development workflows.
