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Prevalence Threshold and bounds in the Accuracy of Binary Classification Systems
The accuracy of binary classification systems is defined as the proportion of correct predictions - both positive and negative - made by a classification model or computational algorithm. A value between 0 (no accuracy) and 1 (perfect accuracy), the accuracy of a classification model is dependent on several factors, notably: the classification rule or algorithm used, the intrinsic characteristics of the tool used to do the classification, and the relative frequency of the elements beingdoi:10.48550/arxiv.2112.13289 fatcat:3wf3bgzj2ffmnillypz5h2hori