ML Anthology

EXTREMUM: A Web-Based Tool to Generate and Explore Counterfactual Explanations on Tabular and Time-Series Data

Athanasios Lakes, Luis Quintero, Panagiotis Papapetrou
ECML-PKDD 2025 pp. 491-496
doi:10.1007/978-3-032-06129-4_37 /ecmlpkdd/2025/lakes2025ecmlpkdd-extremum/

Abstract

There is an increasing need to include explainability on the machine learning (ML) models. Among the various approaches, counterfactual (CF) explanations allow the design of what-if scenarios and the interactive exploration of ML model behavior on sensitive decision-making domains. However, the generation of CF for tabular and time-series data requires technical skills that are not always available to the end-users of ML-powered systems. Therefore, we propose a modular web-based tool to easily generate, visualize, and interact with CF on any tabular or time-series dataset. The EXTREMUM platform provides access to state-of-the-art CF algorithms, where users can train ML models and explore CF on their tabular or time-series datasets with an intuitive user interface. The project is instantiated on two tabular datasets within healthcare and five time-series datasets with various domains. The open-source repository lets ML researchers adapt the existing ML tool to new application domains: https://gitea.dsv.su.se/DataScienceGroup/EXTREMUM-demo .

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Cite

Text

Lakes et al. "EXTREMUM: A Web-Based Tool to Generate and Explore Counterfactual Explanations on Tabular and Time-Series Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_37

Markdown

[Lakes et al. "EXTREMUM: A Web-Based Tool to Generate and Explore Counterfactual Explanations on Tabular and Time-Series Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/lakes2025ecmlpkdd-extremum/) doi:10.1007/978-3-032-06129-4_37

BibTeX

@inproceedings{lakes2025ecmlpkdd-extremum,
  title     = {{EXTREMUM: A Web-Based Tool to Generate and Explore Counterfactual Explanations on Tabular and Time-Series Data}},
  author    = {Lakes, Athanasios and Quintero, Luis and Papapetrou, Panagiotis},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2025},
  pages     = {491-496},
  doi       = {10.1007/978-3-032-06129-4_37},
  url       = {https://mlanthology.org/ecmlpkdd/2025/lakes2025ecmlpkdd-extremum/}
}