Multivariate Prediction Intervals for Random Forests

TL;DR

This paper introduces a recalibrated bootstrap method for generating multivariate prediction intervals in random forests, improving uncertainty estimation for multi-objective optimization in engineering and sciences.

Contribution

It presents a novel recalibrated bootstrap approach for multivariate prediction intervals in bagged models, enhancing calibration and efficiency in sequential learning tasks.

Findings

Recalibrated bootstrap produces well-calibrated multivariate prediction intervals.

Application reduces the number of iterations in sequential learning for multi-objective problems.

Method improves uncertainty quantification in machine learning models for complex systems.

Abstract

Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on multiple correlated model outputs as objectives and/or constraints. To better solve these problems, we propose a recalibrated bootstrap method to generate multivariate prediction intervals for bagged models and show that it is well-calibrated. We apply the recalibrated bootstrap to a simulated sequential learning problem with multiple objectives and show that it leads to a marked decrease in the number of iterations required to find a satisfactory candidate. This indicates that the recalibrated bootstrap could be a valuable tool for practitioners using machine learning to optimize systems with multiple competing targets.