Pinball Quantile Regression Trees for Conformal Prediction
Abstract
Conformal prediction, known for providing model-agnostic uncertainty quantification, has become an essential tool with a mushrooming number of applications. An ideal conformal prediction method should be adaptive, data-efficient, and interpretable. To meet these criteria, we propose to improve existing quantile-based conformal methods using Pinball quantile regression trees. Moreover, we introduce a new quantile out-of-bag framework that is shown empirically to provide smaller conformal prediction sets while maintaining rigorous theoretical guarantees for coverage.
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