What question did this study set out to answer?

To determine whether accurate-yet-simple machine learning models can be predicted or proven to exist prior to performing computationally intensive searches.

June 20, 2022Open Access

On the Existence of Simpler Machine Learning Models

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To determine whether accurate-yet-simple machine learning models can be predicted or proven to exist prior to performing computationally intensive searches.
Formulated the Rashomon ratio, defined as the volume of almost-equally-accurate models relative to the entire hypothesis space.
Established an empirical diagnostic heuristic based on whether distinct machine learning algorithms achieve similar predictive performance on a shared dataset.
Demonstrated that a large Rashomon set increases the likelihood that at least one sparse, interpretable model exists with competitive accuracy.
Showed that observing similar empirical accuracy across diverse machine learning methods serves as an indicator that simpler models are present.

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Implication

Theoretical analysis introduces the Rashomon ratio across machine learning problems, indicating interpretable models can often replace complex systems in high-stakes decisions.

Cite This Study

Semenova et al. (2022) studied this question.

synapsesocial.com/papers/6a1c20701567d2fc4d5fa231 https://doi.org/10.1145/3531146.3533232

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Implication

Theoretical analysis introduces the Rashomon ratio across machine learning problems, indicating interpretable models can often replace complex systems in high-stakes decisions.

On the Existence of Simpler Machine Learning Models

Key Points

Discussion

Implication

Cite This Study

On the Existence of Simpler Machine Learning Models

Key Points

Discussion

Implication

Cite This Study

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