André M. Carrington - researchr alias

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Viewing Publication 1 - 6 from 6

2023

Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and ExplanationAndré M. Carrington, Douglas G. Manuel, Paul W. Fieguth, Tim Ramsay, Venet Osmani, Bernhard Wernly, Carol Bennett, Steven Hawken, Olivia Magwood, Yusuf Sheikh, Matthew McInnes, Andreas Holzinger. pami, 45(1):329-341, 2023. [doi]

2022

The ROC Diagonal is Not Layperson's Chance: A New Baseline Shows the Useful AreaAndré M. Carrington, Paul W. Fieguth, Franz Mayr, Nick D. James, Andreas Holzinger, John W. Pickering, Richard I. Aviv. cdmake 2022: 100-113 [doi]

2020

A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithmsAndré M. Carrington, Paul W. Fieguth, Hammad Qazi, Andreas Holzinger, Helen H. Chen, Franz Mayr, Douglas G. Manuel. midm, 20(1):4, 2020. [doi]

Measuring the Quality of Explanations: The System Causability Scale (SCS)Andreas Holzinger, André M. Carrington, Heimo Müller. ki, 34(2):193-198, 2020. [doi]

2018

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health CareAndré M. Carrington. PhD thesis, University of Waterloo, Ontario, Canada, 2018. [doi]

Measures of Model Interpretability for Model SelectionAndré M. Carrington, Paul W. Fieguth, Helen H. Chen. cdmake 2018: 329-349 [doi]

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