文摘

解释和诊断机器学习模型获得了新的兴趣近年来新方法的突破。我们目前的总管,一个框架,利用视觉分析技术支持解释,调试和机器学习模型的比较更加透明和互动的方式。传统的技术通常专注于可视化的内部逻辑一个特定的模型类型(即。,deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models’ outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.

作者

加威,阳王,皮耶罗Molino,Lezhi李大卫·s·艾伯特

会议

IEEE VIS 2018

论文全文

廖:Model-Agnostic解释和诊断机器学习模型的框架(PDF)

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