DEMAU: Decompose, Explore, Model and Analyse Uncertainties

• URL: http://arxiv.org/abs/2409.08105v1
• Date: Thu, 12 Sep 2024 14:57:28 GMT
• Title: DEMAU: Decompose, Explore, Model and Analyse Uncertainties
• Authors: Arthur Hoarau, Vincent Lemaire,
• Abstract summary: DEMAU is an open-source educational, exploratory and analytical tool allowing to visualize and explore several types of uncertainty for classification models in machine learning.
• Score: 0.8287206589886881
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent research in machine learning has given rise to a flourishing literature on the quantification and decomposition of model uncertainty. This information can be very useful during interactions with the learner, such as in active learning or adaptive learning, and especially in uncertainty sampling. To allow a simple representation of these total, epistemic (reducible) and aleatoric (irreducible) uncertainties, we offer DEMAU, an open-source educational, exploratory and analytical tool allowing to visualize and explore several types of uncertainty for classification models in machine learning.

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