Boxer: Interactive Comparison of Classifier Results
Boxer: Interactive Comparison of Classifier Results
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DOI：
10.1111/cgf.13972
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发表时间：
2020-04
影响因子：
2.5
通讯作者：
Michael Gleicher;Aditya Barve;Xinyi Yu;Florian Heimerl
中科院分区：
文献类型：
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作者：
Michael Gleicher;Aditya Barve;Xinyi Yu;Florian Heimerl
机器学习从业者经常比较不同分类器的结果，以帮助选择、诊断和调整模型。我们目前拳击手，一个系统，使这种比较。我们的系统有利于交互式探索的实验结果，通过应用多个分类器的一组共同的模型输入。该方法的重点是允许用户识别训练和测试实例的有趣子集，并比较这些子集上的分类器的性能。该系统将标准视觉设计与集合代数交互和比较元素相耦合。这允许用户组成和协调视图，以指定子集并评估分类器的性能。这些组合物的灵活性允许用户在开发和评估分类器时解决各种各样的情况。我们在用例中演示Boxer，包括模型选择，调优，公平性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.