Welcome to DivExplorer!

DivExplorer is a tool for analyzing datasets and finding subgroups of data where a classifier behaves differently than on the overall data. To start, please upload a discretized dataset with both the output of a binary classifier, and the (binary) ground truth.

DivExplorer is brought to you by Eliana Pastor, Andrew Gavgavian, Elena Baralis, Luca de Alfaro, and is described in the upcoming paper:
Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence. Eliana Pastor, Luca de Alfaro, Elena Baralis. Proceedings of the 2021 International Conference on Management of Data (SIGMOD ‘21). June 20–25, 2021, Virtual Event, China.