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The machine learning modeling process conventionally culminates in selectinga single model that maximizes a selected performance metric. However, thisapproach leads to abandoning a more profound analysis of slightly inferiormodels. Particularly in medical and healthcare studies, where the objectiveextends beyond predictions to valuable insight generation, relying solely on asingle model can result in misleading or incomplete conclusions. This problemis particularly pertinent when dealing with a set of models known as$\textit{Rashomon set}$, with performance close to maximum one. Such a set canbe numerous and may contain models describing the data in a different way,which calls for comprehensive analysis. This paper introduces a novel processto explore models in the Rashomon set, extending the conventional modelingapproach. We propose the $\texttt{Rashomon_DETECT}$ algorithm to detect modelswith different behavior. It is based on recent developments in the eXplainableArtificial Intelligence (XAI) field. To quantify differences in variableeffects among models, we introduce the Profile Disparity Index (PDI) based onmeasures from functional data analysis. To illustrate the effectiveness of ourapproach, we showcase its application in predicting survival amonghemophagocytic lymphohistiocytosis (HLH) patients - a foundational case study.Additionally, we benchmark our approach on other medical data sets,demonstrating its versatility and utility in various contexts. If differentlybehaving models are detected in the Rashomon set, their combined analysis leadsto more trustworthy conclusions, which is of vital importance for high-stakesapplications such as medical applications.

Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data