DEMAU: Decompose, Explore, Model and Analyse Uncertainties
This tool addresses the problem of uncertainty representation for researchers and practitioners in machine learning, particularly in educational and exploratory contexts, but it is incremental as it builds on existing literature without introducing new methods.
The authors tackled the need for visualizing and exploring different types of model uncertainty in machine learning by developing DEMAU, an open-source tool that allows users to decompose and analyze total, epistemic, and aleatoric uncertainties for classification models.
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.