The base rate is the underlying frequency or proability of a phenomena. For example, if one in thousand people are likely to be suffering from flu during the windter, then the base rate of flu is 0.001. The base rate is critical when using evidence to make diagnoses or ither decisions. For example, if flu and the common cold have similar symptoms and ther base rate of the common cold is one in 50, then someone with a runny nose and headache is more likely to be suffering form a cold than flu. This is captured formally in Bayes Theorem.
<.br>Base rates are also important when trying to reduce bias or discrimination as we try to be fair to an individual irrespectve of the base rate for their demographic. Without taking remedial action data-based machine learning will tend to be discriminatory becasue of this, even if the data is free of human bias.
Used on pages 174, 466, 504, 513