<ul data-eligibleForWebStory="false"><li>Conformal prediction is a framework for uncertainty quantification that constructs prediction sets with coverage guarantees, often requiring a holdout set for parameter tuning.</li><li>Empirical findings suggest that the tuning bias, resulting from using the same dataset for tuning and calibration, is minimal for simple parameter tuning in many conformal prediction methods.</li><li>A scaling law for tuning bias is observed, showing that bias increases with parameter space complexity but decreases with calibration set size.</li><li>The study establishes a theoretical framework to quantify tuning bias, provides a proof for the scaling law, and discusses strategies to mitigate tuning bias based on the research findings.</li></ul>

Parametric Scaling Law of Tuning Bias in Conformal Prediction

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