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The Rashomon set is the set of models that perform approximately equally wellon a given dataset, and the Rashomon ratio is the fraction of all models in agiven hypothesis space that are in the Rashomon set. Rashomon ratios are oftenlarge for tabular datasets in criminal justice, healthcare, lending, education,and in other areas, which has practical implications about whether simplermodels can attain the same level of accuracy as more complex models. An openquestion is why Rashomon ratios often tend to be large. In this work, wepropose and study a mechanism of the data generation process, coupled withchoices usually made by the analyst during the learning process, thatdetermines the size of the Rashomon ratio. Specifically, we demonstrate thatnoisier datasets lead to larger Rashomon ratios through the way thatpractitioners train models. Additionally, we introduce a measure called patterndiversity, which captures the average difference in predictions betweendistinct classification patterns in the Rashomon set, and motivate why it tendsto increase with label noise. Our results explain a key aspect of why simplermodels often tend to perform as well as black box models on complex, noisierdatasets.

A Path to Simpler Models Starts With Noise