Understanding the math of regression models
As we saw in the previous chapter, regression problems are a type of supervised learning problem whose output is a number from a continuous distribution, such as the price of a house or the predicted value of a company stock price.
The simplest model we can use for a regression problem is a linear regression model. However, these models are extremely powerful for simple problems, as their parameters can be trained and are very fast and explainable, given the small number of parameters involved. As we will see, this number of parameters is completely dependent on the number of features we use.
Another interesting property of linear regression models is that they can be represented by neural networks, and as neural networks will be the basis for most models that we will be using throughout the book, this is the linear regression model based on neural networks that we will be using.
