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Prevalence Adjustment in Binary Classifiers

David Lindelöf deal with an issue in analyzing classification models:

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. 

Read on to learn what this means for precision, as well as one technique for tracking prevalence changes over itme.

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