Classification methods for binary (yes/no) tasks often produce a continuously valued score. Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment. Such tasks involve examining classifier results, typically using summary statistics and manual examination of details. In this paper, we provide an interactive visualization approach to support such continuously-valued classifier examination tasks. Our approach addresses the three phases of these tasks: calibration, operating point selection, and examination. We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination (MVC) system. We build on an existing comparison-based approach, extending it to continuous classifiers by treating the continuous values as trinary (positive, unsure, negative) even if the classifier will not ultimately use the 3-way classification. We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks.

翻译：二进制(是/否)任务分类方法往往产生持续价值的得分。机器学习实践者必须执行模式选择、校准、分解、业绩评估、调适和公平评估。这些任务包括审查分类结果,通常使用简要统计和对细节的手工检查。在本文件中,我们提供了一种互动可视化方法来支持这种不断估值的分类工作。我们的方法涉及这些任务的三个阶段:校准、操作点选择和考试。我们加强了标准观点并引入了具体任务的观点,以便将其纳入多视图协调(监查)系统。我们以现有的以比较为基础的方法为基础,通过将连续值作为三进制(积极、不确定、否定)处理,将其扩大到连续的分类者。我们提供了实例,说明我们的方法如何使机器学习实践者能够完成关键任务。