DEMAU可可视化分解分类模型的各类不确定性，助力主动学习与决策。
DEMAU: Decompose, Explore, Model and Analyse Uncertainties
- 分三类展示模型不确定性：总、认知性（可减少）、随机性（不可减少）
- 支持交互式探索，帮助理解不确定性的来源与分布
- 开源工具，适合教学、研究及模型调试场景
机器学习领域对模型不确定性的量化与分解研究日益丰富。此类信息在主动学习、自适应学习及不确定性采样中极具价值。为简化对总不确定性、认知性不确定性（可减少）和随机性不确定性（不可减少）的表示，本文提出DEMAU——一个开源的教育性、探索性与分析性工具，可对分类模型中的多种不确定性进行可视化与交互式探索。
原文摘要 · Abstract (English)
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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