Boosted Conformal Prediction Intervals

• URL: http://arxiv.org/abs/2406.07449v2
• Date: Sat, 09 Nov 2024 18:37:37 GMT
• Title: Boosted Conformal Prediction Intervals
• Authors: Ran Xie, Rina Foygel Barber, Emmanuel J. Candès,
• Abstract summary: We employ machine learning techniques to improve upon a predefined conformity score function. The boosted conformal procedure achieves substantial improvements in reducing interval length and decreasing deviation from target conditional coverage.
• Score: 5.762286612061953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length. We employ machine learning techniques, notably gradient boosting, to systematically improve upon a predefined conformity score function. This process is guided by carefully constructed loss functions that measure the deviation of prediction intervals from the targeted properties. The procedure operates post-training, relying solely on model predictions and without modifying the trained model (e.g., the deep network). Systematic experiments demonstrate that starting from conventional conformal methods, our boosted procedure achieves substantial improvements in reducing interval length and decreasing deviation from target conditional coverage.

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