MLMC让多标签分类器评估更直观，无需混乱矩阵。
MLMC: Interactive multi-label multi-classifier evaluation without confusion matrices
- 用可视化工具替代难扩展的混淆矩阵
- 支持从实例、标签、分类器三个角度分析性能
- 适合需要深度评估多标签分类结果的研究者
基于机器学习的分类器常通过准确率等指标评估，但需深入分析其优劣。MLMC是一种视觉探索工具，用于解决多标签分类器比较与评估的挑战。它提供了一种可扩展的替代方案，取代了传统上在类别或标签数量大时难以扩展的混淆矩阵。此外，MLMC允许用户从实例视角、标签视角和分类器视角查看性能表现。用户研究表明，该工具在保持友好易用的同时，实现了强大的多标签分类器评估能力。
原文摘要 · Abstract (English)
Machine learning-based classifiers are commonly evaluated by metrics like accuracy, but deeper analysis is required to understand their strengths and weaknesses. MLMC is a visual exploration tool that tackles the challenge of multi-label classifier comparison and evaluation. It offers a scalable alternative to confusion matrices which are commonly used for such tasks, but don't scale well with a large number of classes or labels. Additionally, MLMC allows users to view classifier performance from an instance perspective, a label perspective, and a classifier perspective. Our user study shows that the techniques implemented by MLMC allow for a powerful multi-label classifier evaluation while preserving user friendliness.
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