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\subsection{Making Predictions}  As stated in the overview, logistic regression gives a model whose output is the probabilty probability  of y=1. To actually predict if the instance is in the class or not, the user must choose a cutoff value, say 0.5. Then we can say that $y = 1$ for $p(x)>0.5$. In the case of only one predictor variable, this corresponds to choosing a cutoff x value. With two predictors, a line is choosen chosen  that divides the values in and out of the class. And so on for higher dimensions. This is called the decision boudary, boundary,  and the classification becomes clear. \subsection{Multiple Predictors}