Remember when I said, *a thorough understanding of linear models will pay enormous dividends throughout your career as an analyst* in the previous chapter? Well, I wasn't lying! This next classifier is a product of a generalization of linear regression that can act as a classifier.

What if we used linear regression on a binary outcome variable, representing diabetes as *1* and not diabetes as *0*? We know that the output of linear regression is a continuous prediction, but what if, instead of predicting the binary class (diabetes or not diabetes), we attempted to predict the *probability* of an observation having diabetes? So far, the idea is to train a linear regression on a training set where the variables we are trying to predict ...

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