DTs normally fall under supervised learning techniques, which are used to identify and solve problems related to classification and regression. As the name indicates, DTs have various branches—where each branch indicates a possible decision, appearance, or reaction in terms of statistical probability. In terms of features, DTs are split into two main types: the training set and the test set, which helps produce a good update on the predicted labels or classes.
Both binary and multiclass classification problems can be handled by DT algorithms, which is one of the reasons it is used across problems. For instance, for the admission example we introduced in Chapter 3, Scala for Learning Classification, DTs learn from the admission data to approximate a sine curve with a set of if...else decision rules, as shown in the following diagram:
