Showing posts from April, 2021

Why model calibration matters and how to achieve it

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. This post explains why calibration matters, and how to achieve it. It discusses practical issues that calibrated predictions solve and presents a flexible framework to calibrate any classifier. Calibration applies in many applications, and hence the practicing data scientist must understand this useful tool. What is calibration? At Google we make predictions for a large number of binary events such as “will a user click this ad” or “is this email spam”. In addition to the raw classification of $Y = 0$/'NotSpam' or $Y = 1$/'Spam' we are also interested in predicting the probability of the binary event $\Pr(Y = 1 | X)$ for some covariates $X$. One useful property of these predictions is calibration. To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that i