Logistic regression
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.
Problem domain of linear regression and logistic regression
To intuitively understand the problem domain of the logistic regression, we will employ a graphical representation.
In the first we show the linear fitting function, which is the main objective of the whole model building process, and at the bottom, the target data distribution. As you clearly...
