Data as of Jul 25, 2026 · Based on 2,718,867 AI responses across 9,511 prompts · See how Parse measures this
DivExplorerdivexplorer.github.ioDivExplorer is a project that enables the analysis of subgroup performance in datasets, efficiently identifying anomalous data subgroups with higher or lower average attribute values. It also helps in machine learning to find subgroups where classifiers have higher false positive or false negative rates than average.
Parse Score
Sources
arxiv.org shapes more of what AI says about DivExplorer than any other source, at 67% of its citations.
pmc.ncbi.nlm.nih.gov
