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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Logistic regression

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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