Your Classifier Is Broken, But It Is Still Useful

When you run a binary classifier over a population you get an estimate of the proportion of true positives in that population. This is known as the prevalence. But that estimate is biased, because no classifier is perfect. For example, if your classifier tells you that you have 20% of positive …