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Tetrahedron: Barycentric Measure Visualizer

  • Dariusz BrzezinskiEmail author
  • Jerzy Stefanowski
  • Robert Susmaga
  • Izabela Szczȩch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)

Abstract

Each machine learning task comes equipped with its own set of performance measures. For example, there is a plethora of classification measures that assess predictive performance, a myriad of clustering indices, and equally many rule interestingness measures. Choosing the right measure requires careful thought, as it can influence model selection and thus the performance of the final machine learning system. However, analyzing and understanding measure properties is a difficult task. Here, we present Tetrahedron, a web-based visualization tool that aids the analysis of complete ranges of performance measures based on a two-by-two contingency matrix. The tool operates in a barycentric coordinate system using a 3D tetrahedron, which can be rotated, zoomed, cut, parameterized, and animated. The application is capable of visualizing predefined measures (86 currently), as well as helping prototype new measures by visualizing user-defined formulas.

Notes

Acknowledgments

NCN DEC-2013/11/B/ST6/00963, PUT Statutory Funds.

References

  1. 1.
    Brzezinski, D., Stefanowski, J., Susmaga, R., Szczȩch, I.: Visual-based analysis of classification measures with applications to imbalanced data. arXiv:1704.07122
  2. 2.
    Susmaga, R., Szczȩch, I.: Can interestingness measures be usefully visualized? Int. J. Appl. Math. Comp. Sci. 25(2), 323–336 (2015)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dariusz Brzezinski
    • 1
    Email author
  • Jerzy Stefanowski
    • 1
  • Robert Susmaga
    • 1
  • Izabela Szczȩch
    • 1
  1. 1.Institute of Computing Science, Poznan University of TechnologyPoznanPoland

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