Aleatoric and Epistemic Uncertainty with Random Forests

01/03/2020
by   Mohammad Hossein Shaker, et al.
2

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/21/2019

Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction

The notion of uncertainty is of major importance in machine learning and...
research
11/07/2016

One Class Splitting Criteria for Random Forests

Random Forests (RFs) are strong machine learning tools for classificatio...
research
05/26/2023

Sources of Uncertainty in Machine Learning – A Statisticians' View

Machine Learning and Deep Learning have achieved an impressive standard ...
research
08/23/2022

Evaluating Machine Unlearning via Epistemic Uncertainty

There has been a growing interest in Machine Unlearning recently, primar...
research
06/15/2022

Epistemic Deep Learning

The belief function approach to uncertainty quantification as proposed i...
research
06/23/2023

Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

To achieve virtual certification for industrial design, quantifying the ...

Please sign up or login with your details

Forgot password? Click here to reset