Training and predicting with machine learning models

In Chapter 5, Time Series Forecasting as Regression, we talked about a schematic for supervised machine learning (Figure 5.2). In the schematic, we mentioned that the purpose of a supervised learning problem is to come up with a function, , where X is the set of features as the input, is the model parameters, and h is the approximation of the ideal function. In this section, we are going to talk about h in more detail and see how we can use different machine learning models to estimate it.

h is any function that approximates the ideal function, but it can be thought of as an element of all possible functions from a family of functions. More formally, we can say the following:

Here, is a family of functions that we also call a model. For instance, linear regression is a type of model or a family of functions. For each value of the coefficients, the linear regression model gives you a different function...