论文标题
HYPOML：基于假设的机器学习模型评估的视觉分析
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models
论文作者
论文摘要
在本文中，我们提出了一种视觉分析工具，用于基于假设的机器学习评估（ML）模型。我们描述了一个新型的ML测试框架，该框架结合了传统的统计假设检验（经验研究中通常使用）与关于多个假设结论的逻辑推理。该框架定义了一种受控配置，用于测试许多关于“概念”或“特征”的额外信息以及如何受益或阻碍ML模型的一些额外信息。因为推理多个假设并不总是直接的，所以我们将HYPOML作为视觉分析工具提供，将多线程测试数据转化为视觉表示，以快速观察结论和测试数据和假设之间的逻辑流程。我们已将hypoml应用于假设概念的许多概念，以示意和解释的视觉分析，以进行视觉分析。
In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a "concept" or "feature" may benefit or hinder a ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing data is transformed to a visual representation for rapid observation of the conclusions and the logical flow between the testing data and hypotheses.We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.
