HypoML: Visual analysis for hypothesis-based evaluation of machine learning models

Qianwen Wang, William Alexander, Jack Pegg, Huamin Qu, Min Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article number9222284
Pages (from-to)1417-1426
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number2
DOIs
StatePublished - Feb 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1995-2012 IEEE.

Keywords

  • HypoML
  • hypothesis test
  • machine learning
  • model-developmental visualization
  • neural network
  • Visual analytics

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