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- W3018928030 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, referring, respectively, to inherent randomness and a lack of knowledge, 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." @default.
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- W3018928030 date "2020-01-01" @default.
- W3018928030 modified "2023-09-30" @default.
- W3018928030 title "Aleatoric and Epistemic Uncertainty with Random Forests" @default.
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- W3018928030 doi "https://doi.org/10.1007/978-3-030-44584-3_35" @default.
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