  Classification is one of the most important supervised machine learning
tasks. During the training of a classification model, the training instances
are fed to the model multiple times (during multiple epochs) in order to
iteratively increase the classification performance. The increasing complexity
of models has led to a growing demand for model interpretability through
visualizations. Existing approaches mostly focus on the visual analysis of the
final model performance after training and are often limited to aggregate
performance measures. In this paper we introduce InstanceFlow, a novel
dual-view visualization tool that allows users to analyze the learning behavior
of classifiers over time on the instance-level. A Sankey diagram visualizes the
flow of instances throughout epochs, with on-demand detailed glyphs and traces
for individual instances. A tabular view allows users to locate interesting
instances by ranking and filtering. In this way, InstanceFlow bridges the gap
between class-level and instance-level performance evaluation while enabling
users to perform a full temporal analysis of the training process.
