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Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations.

, , and . CoRR, (2019)

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The ROC Diagonal is Not Layperson's Chance: A New Baseline Shows the Useful Area., , , , , , and . CD-MAKE, volume 13480 of Lecture Notes in Computer Science, page 100-113. Springer, (2022)Measures of Model Interpretability for Model Selection., , and . CD-MAKE, volume 11015 of Lecture Notes in Computer Science, page 329-349. Springer, (2018)Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care.. University of Waterloo, Ontario, Canada, (2018)base-search.net (ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/13735).Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation., , , , , , , , , and 2 other author(s). IEEE Trans. Pattern Anal. Mach. Intell., 45 (1): 329-341 (2023)Measuring the Quality of Explanations: The System Causability Scale (SCS)., , and . Künstliche Intell., 34 (2): 193-198 (2020)A new Mercer sigmoid kernel for clinical data classification., , and . EMBC, page 6397-6401. IEEE, (2014)Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations., , and . CoRR, (2019)Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation., , , , , , , , , and 2 other author(s). CoRR, (2021)A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms., , , , , , and . BMC Medical Informatics Decis. Mak., 20 (1): 4 (2020)