HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models.

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ID: 171486
2020
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Ranked #20 of 32 articles by views in ieee transactions on visualization and computer graphics

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Abstract
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 an ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing results are first transformed to analytical results using statistical and logical inferences, and then to a visual representation for rapid observation of the conclusions and the logical flow between the testing results and hypotheses. We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.
Reference Key
wang2020hypomlieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Qianwen;Alexander, William;Pegg, Jack;Qu, Huamin;Chen, Min;
Journal ieee transactions on visualization and computer graphics
Year 2020
DOI
10.1109/TVCG.2020.3030449
URL
Keywords
algorithm hepatocellular carcinoma (hcc) machine learning (ml) next-generation sequencing (ngs) hepatitis b virus (hbv)

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