  Machine learning on data streams is increasingly more present in multiple
domains. However, there is often data distribution shift that can lead machine
learning models to make incorrect decisions. While there are automatic methods
to detect when drift is happening, human analysis, often by data scientists, is
essential to diagnose the causes of the problem and adjust the system. We
propose Data+Shift, a visual analytics tool to support data scientists in the
task of investigating the underlying factors of shift in data features in the
context of fraud detection. Design requirements were derived from interviews
with data scientists. Data+Shift is integrated with JupyterLab and can be used
alongside other data science tools. We validated our approach with a
think-aloud experiment where a data scientist used the tool for a fraud
detection use case.

Machine Learning;Human-Computer Interaction