A False Positive Rate is a metric that may be used to assess the accuracy of a subset of machine learning models. A model must have some idea of “ground truth,” or the genuine state of things, in order to get a reading on its true correctness. The accuracy of models can thus be directly measured by comparing their outputs to the ground truth.