Abstract
Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.
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