Vassilis Stamatopoulos, Panos Gidarakos, Stavros Maroulis, George Papastefanatos, Panos Vassiliadis.
Experiment tracking systems log hundreds of runs with large numbers of (a) configuration parameters for the fine-tuning of the experiment and (b) a large number of metrics that assess the behavior of each configuration. However, the large numbers of runs makes it difficult to discover the trade-offs of each configuration to its behavior, towards deciding the right configuration that satisfies an analyst’s intent. We address this problem with an automated experiment analytics approach that (i) groups runs into behaviorally consistent clusters based on their observed metrics and (ii) generates compact, interpretable cluster-level summaries that connect characteristic metric outcomes to the configuration choices that tend to produce them. For each discovered behavior, the method produces actionable highlights, including representative metrics, salient metric relationships, and concise configuration rules that describe where the behavior occurs in the configuration space. Visualizations with radar charts and parallel coordinates provide an interactive means to highlight the pros and cons of each behavior with respect to its representative metrics and configuration. Experiments on several ML pipelines, show that the method yields strong cluster structure (Silhouette Score > 0.9) and stable descriptors using only 3–6 representative metrics per cluster, while remaining computationally practical with end-to-end runtimes on the order of seconds. An ablation study further shows that removing key components degrades at least one aspect of performance or interpretability, underscoring the importance of the overall approach.
Vassilis Stamatopoulos, Panos Gidarakos, Stavros Maroulis, George Papastefanatos, Panos Vassiliadis. Interpretable Highlights for Experiment Tracking. 28th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP). Co-located with EDBT/ICDT 2026, Tampere, Finland - March 24, 2026
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More details at: [DataStory: a paradigm shift for data analytics]