Understanding math for classification models
As we saw in the previous chapter, classification problems are supervised learning problems whose output is a class from a set of classes (categorical assignments) – for example, the iris class of a flower.
As we will see throughout this recipe, classification models can be seen as individual cases of regression models. We will start by exploring a binary classification model. This is a model that will output one of two classes. We will label these classes [0, 1] for simplicity.
The simplest model we can use for such a binary classification problem is a linear regression model. This model will output a number; therefore, to modify the output to satisfy our new classification criteria, we will modify the activation function to a more suitable one.
As in the previous recipes, we will use a neural network as our model, and we will solve the iris dataset prediction problem we introduced in the second recipe, Toy dataset for classification...
