  Concept drift is a phenomenon in which the distribution of a data stream
changes over time in unforeseen ways, causing prediction models built on
historical data to become inaccurate. While a variety of automated methods have
been developed to identify when concept drift occurs, there is limited support
for analysts who need to understand and correct their models when drift is
detected. In this paper, we present a visual analytics method, DriftVis, to
support model builders and analysts in the identification and correction of
concept drift in streaming data. DriftVis combines a distribution-based drift
detection method with a streaming scatterplot to support the analysis of drift
caused by the distribution changes of data streams and to explore the impact of
these changes on the model's accuracy. A quantitative experiment and two case
studies on weather prediction and text classification have been conducted to
demonstrate our proposed tool and illustrate how visual analytics can be used
to support the detection, examination, and correction of concept drift.
