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Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance

1
Department of Electrical and Electronic Engineering, University of Cagliari, 09124 Cagliari, Italy
2
Department of Mathematics & Computer Science, University of Marburg, 35037 Marburg, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Mach. Learn. Knowl. Extr. 2019, 1(1), 121-137; https://doi.org/10.3390/make1010007
Received: 15 May 2018 / Revised: 22 June 2018 / Accepted: 27 June 2018 / Published: 28 June 2018
(This article belongs to the Section Visualization)
In this article, a two-tiered 2D tool is described, called φ,δ diagrams, and this tool has been devised to support the assessment of classifiers in terms of accuracy and bias. In their standard versions, these diagrams provide information, as the underlying data were in fact balanced. Their generalization, i.e., ability to account for the imbalance, will be also briefly described. In either case, the isometrics of accuracy and bias are immediately evident therein, as—according to a specific design choice—they are in fact straight lines parallel to the x-axis and y-axis, respectively. φ,δ diagrams can also be used to assess the importance of features, as highly discriminant ones are immediately evident therein. In this paper, a comprehensive introduction on how to adopt φ,δ diagrams as a standard tool for classifier and feature assessment is given. In particular, with the goal of illustrating all relevant details from a pragmatic perspective, their implementation and usage as Python and R packages will be described. View Full-Text
Keywords: feature importance; classifier performance measures; confusion matrices; ROC curves; R-package; Python package feature importance; classifier performance measures; confusion matrices; ROC curves; R-package; Python package
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Armano, G.; Giuliani, A.; Neumann, U.; Rothe, N.; Heider, D. Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance. Mach. Learn. Knowl. Extr. 2019, 1, 121-137.

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