We've already discussed the theory behind logistic regression, so we can begin fitting our models. An R installation comes with the glm() function fitting the generalized linear models, which are a class of models that includes logistic regression. The code syntax is similar to the lm() function that we used in the previous chapter. One big difference is that we must use the family = binomial argument in the function, which tells R to run a logistic regression method instead of the other versions of the generalized linear models. We will start by creating a model that includes all of the features on the train set and see how it performs on the test set, as follows:
> full.fit <- glm(class ~ ., family = binomial, ...
