Aleatoric and Epistemic Uncertainty with Random Forests

TL;DR

This paper explores methods to quantify aleatoric and epistemic uncertainty in predictions using random forests, comparing their effectiveness with deep neural networks in classification tasks.

Contribution

It introduces approaches to measure both types of uncertainty with random forests and compares their performance to neural networks.

Findings

Random forests can effectively quantify aleatoric and epistemic uncertainty.

The proposed methods are comparable to deep neural networks in uncertainty estimation.

Random forests offer a potentially more interpretable alternative for uncertainty quantification.

Abstract

Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learner's aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.