Many problem domains utilize discriminant analysis, for example, classification, prediction, and diagnoses, by applying artificial\nintelligence and machine learning. However, the results are rarely perfect and errors can cause significant losses. Hence, end users\nare best served when they have performance information relevant to their need. Starting with the most basic questions, this study\nconsiders eight summary statistics often seen in the literature and evaluates their end user efficacy. Results lead to proposed criteria\nnecessary for end user efficacious summary statistics. Testing the same eight summary statistics shows that none satisfy all of the\ncriteria. Hence, two criteria-compliant summary statistics are introduced. To show how end users can benefit, measure utility is\ndemonstrated on two problems. A key finding of this study is that researchers can make their test outcomes more relevant to end\nusers with minor changes in their analyses and presentation.
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