Boosted Conformal Prediction Intervals

TL;DR

This paper presents a boosted conformal prediction method that uses machine learning to improve the properties of prediction intervals, such as coverage and length, without retraining the underlying model.

Contribution

It introduces a novel boosting approach for conformal prediction that enhances interval properties post-training using gradient boosting techniques.

Findings

Significant reduction in interval length.

Improved conditional coverage accuracy.

Operates without retraining the original model.

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.