The way of this book is one of generalizations. In the first chapter, we began with simpler representations of the reality, and so simpler criteria for grouping or predicting information structures.
After having reviewed linear regression, which is used mainly to predict a real value following a modeled linear function, we will advance to a generalization of it, which will allow us to separate binary outcomes (indicating that a sample belongs to a class), starting from a previously fitted linear function. So let's get started with this technique, which will be of fundamental use in almost all the following chapters of this book.
