Nested conformal prediction and quantile out-of-bag ensemble methods

作者：

Highlights：

• We study methods for quantifying predictive uncertainty in machine learning.

• Conformal prediction is a technique for forming distribution-free prediction intervals.

• Nested conformal is an intuitive rephrasing of conformal in terms of nested sets.

• Our algorithm QOOB combines quantile random forests with the leave-one-out jackknife.

• QOOB achieves state-of-the-art results for conformal prediction in regression.

摘要

•We study methods for quantifying predictive uncertainty in machine learning.•Conformal prediction is a technique for forming distribution-free prediction intervals.•Nested conformal is an intuitive rephrasing of conformal in terms of nested sets.•Our algorithm QOOB combines quantile random forests with the leave-one-out jackknife.•QOOB achieves state-of-the-art results for conformal prediction in regression.

论文关键词：Conformal prediction,Quantile regression,Cross-conformal,Out-of-bag methods,Ensemble methods,Random forests

论文评审过程：Received 17 May 2020, Revised 19 February 2021, Accepted 14 April 2021, Available online 29 December 2021, Version of Record 23 March 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108496