What is a False Positive Rate?

A False Positive Rate is an accuracy metric that can be measured on a subset of machine learning models. In order to get a reading on true accuracy of a model, it must have some notion of "ground truth", i.e. the true state of things. Accuracy can then be directly measured by comparing the outputs of models with this ground truth. This is usually possible with supervised learning methods, where the ground truth takes the form of a set of labels that describe and define the underlying data. One such supervised learning technique is classification, where the labels are a discrete set of classes that describe individual data points. The classifier will predict the most likely class for new data based on what it has learned about historical data. Since the data is fully labeled, the predicted value can be checked against the actual label (i.e. the ground truth) to measure the accuracy of the model.