🤖 AI Summary
This work addresses the low data efficiency and high computational overhead of conformal prediction caused by its reliance on an independent calibration set. We propose an end-to-end stacked ensemble conformalization method that embeds conformal prediction into a stacking framework, employing a lightweight top-layer meta-learner to directly model nonconformity scores—eliminating the need for calibration-set splitting while achieving approximate marginal coverage guarantees. Theoretical analysis establishes statistical validity under mild assumptions. Empirical evaluation across multiple benchmark datasets demonstrates that our approach yields more stable coverage, reduced calibration error, and significantly lower inference and conformalization costs compared to standard inductive conformal prediction. To the best of our knowledge, this is the first method to jointly realize end-to-end conformalization of stacked ensembles, effectively balancing statistical rigor with engineering practicality.
📝 Abstract
We consider the conformalization of a stacked ensemble of predictive models, showing that the potentially simple form of the meta-learner at the top of the stack enables a procedure with manageable computational cost that achieves approximate marginal validity without requiring the use of a separate calibration sample. Empirical results indicate that the method compares favorably to a standard inductive alternative.