Classification algorithms study how to automatically learn to make accurate predictions based on observations. Starting from a set of predefined class labels, the algorithm gives each piece of data input a class label in accordance with the training model. If there are just two distinction classes, we talk about binary classification; otherwise, we go for multi-class classification. In more detail, each category corresponds to a different label; the algorithm attaches a label to each instance, which simply indicates which class the data belongs to. A procedure that can perform this function is commonly called a classifier.
Classification has some analogy with regression, which we studied in Chapter 4, Finding Relationships between Variables - Regression Techniques. As well as regression, classification uses known labels of a training dataset to predict the response of the new test dataset. The main difference between regression...