In this section, we explain how the linear regression model can be used from a Machine Learning perspective to predict a numerical target variable. We use the concepts of loss function and empirical risk minimization to find the linear model that fits the data best.
This section introduces k-nearest neighbors regression. We will explain in which sense this approach is fundamentally different to the previous sections. We will introduce the concept of distance measures and give examples for different types of data.