<ul><li>The Rashomon effect in machine learning highlights the variations in how different models achieve similar performance while explaining relationships differently.</li><li>Even interpretable models like Generalized Additive Models (GAMs) can exhibit multiple configurations with similar performance, prompting the need for personalized adjustments for interpretability.</li><li>A study developed an approach using contextual bandits to personalize GAM configurations based on users' interpretability needs.</li><li>Results from an online experiment with 108 users showed that personalization resulted in individualized model configurations without compromising interpretability, offering insights into the potential of personalized interpretable machine learning.</li></ul>

Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users

Discover more