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This research identifies a critical vulnerability in AutoML where frameworks like H2O exhibit higher susceptibility to "x-hacking" compared to AutoGluon due to unstable model explanations within Rashomon sets. By introducing the ARSA ML framework, the authors provide a method to quantify this predictive multiplicity and structural asymmetry across standard classification datasets. The findings highlight that equal predictive performance does not guarantee equivalent reliability when models are selected based on their interpretability rather than pure accuracy.
Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages
arXiv:2609.36970v1 Announce Type: new Abstract: The Rashomon effect describes the existence of multiple near-optimal models that achieve comparable performance while offering fundamentally different explanations. This creates a critical vulnerability in AutoML: x-hacking, the selective post-hoc choice of a model based on its explanation rather than predictive merit. No existing AutoML framework exposes this risk. We introduce ARSA ML, an open-source Python framework that quantifies Rashomon set structure and predictive multiplicity within AutoML pipelines. Using ARSA ML, we benchmark AutoGluon and H2O across 28 binary classification datasets, and conduct a post-hoc x-hacking analysis revealing a consistent structural asymmetry: AutoGluon produces larger, diverse sets with stable explanations, while H2O generates compact sets with markedly higher prediction divergence and explanation instability -- making H2O users considerably more exposed to x-hacking. This gap persists across all evaluated metrics and epsilon thresholds, pointing to a fundamental difference in each framework's model-building strategy. ARSA ML is available at https://pypi.org/project/arsa-ml/ .
Abstract:The Rashomon effect describes the existence of multiple near-optimal models that achieve comparable performance while offering fundamentally different explanations. This creates a critical vulnerability in AutoML: x-hacking, the selective post-hoc choice of a model based on its explanation rather than predictive merit. No existing AutoML framework exposes this risk. We introduce ARSA ML, an open-source Python framework that quantifies Rashomon set structure and predictive multiplicity within AutoML pipelines. Using ARSA ML, we benchmark AutoGluon and H2O across 28 binary classification datasets, and conduct a post-hoc x-hacking analysis revealing a consistent structural asymmetry: AutoGluon produces larger, diverse sets with stable explanations, while H2O generates compact sets with markedly higher prediction divergence and explanation instability -- making H2O users considerably more exposed to x-hacking. This gap persists across all evaluated metrics and epsilon thresholds, pointing to a fundamental difference in each framework's model-building strategy. ARSA ML is available at this https URL .
Machine Learning (cs.LG)
arXiv:2609.36970 [cs.LG]
(or arXiv:2609.36970v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.36970 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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