Binary decision making is a topic of great interest for many fields, including biomedical science, economics, management, politics, medicine, natural science and social science, and much effort has been spent for developing novel computational methods to address problems arising in the aforementioned fields. However, in order to evaluate the effectiveness of any prediction method for binary decision making, the choice of the most appropriate error measures is of paramount importance. Due to the variety of error measures available, the evaluation process of binary decision making can be a complex task. The main objective of this study is to provide a comprehensive survey of error measures for evaluating the outcome of binary decision making applicable to many data-driven fields. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningTechnologies > PredictionAlgorithmic Development > Statistics.
Keywords: classification; data science; decision making; error measures; machine learning; statistics.
© 2019 The Authors. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.