Abstract
Classic statistical learning methods are linear and logistic regression. Both methods are supervised learners. That means the target and input features are known. Linear regression is used for predictions, whilst logistic regression is a classifier. These methods are well-established and have many benefits. For instance, a lot of the theory of the methods is understood and the resulting models are easy to interpret. To evaluate the quality of the prediction and classification models, we need to understand the errors. This chapter uses an example to introduce the concept of errors.