判别式
机器学习
线性判别分析
分类器(UML)
尤登J统计
人工智能
计算机科学
接收机工作特性
作者
Marina Sokolova,Nathalie Japkowicz,Stan Śzpakowicz
摘要
Different evaluation measures assess different characteristics of machine learning algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going debate among researchers. Most measures in use today focus on a classifier’s ability to identify classes correctly. We note other useful properties, such as failure avoidance or class discrimination, and we suggest measures to evaluate such properties. These measures – Youden’s index, likelihood, Discriminant power – are used in medical diagnosis. We show that they are interrelated, and we apply them to a case study from the field of electronic negotiations. We also list other learning problems which may benefit from the application of these measures.
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