论文标题
拳击手：分类器结果的交互式比较
Boxer: Interactive Comparison of Classifier Results
论文作者
论文摘要
机器学习从业人员通常比较不同分类器的结果，以帮助选择，诊断和调整模型。我们提出了拳击手，这是一个可以进行这种比较的系统。我们的系统通过将多个分类器应用于一组通用模型输入来促进对实验结果的交互式探索。该方法着重于允许用户识别有趣的培训和测试实例子集，并比较这些子集对分类器的性能。该系统将标准视觉设计与设定代数相互作用和比较元素融合在一起。这允许用户撰写和协调视图以指定子集并评估其上的分类器性能。这些组合的灵活性使用户可以在开发和评估分类器时解决各种方案。我们在用例中展示了拳击手，包括模型选择，调整，公平评估和数据质量诊断。
Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
