Decision tree learners are powerful classifiers that utilize a tree structure to model the relationships among the features and the potential outcomes. As illustrated in the following figure, this structure earned its name due to the fact that it mirrors the way a literal tree begins at a wide trunk and splits into narrower and narrower branches as it is followed upward. In much the same way, a decision tree classifier uses a structure of branching decisions that channel examples into a final predicted class value.
To better understand how this works in practice, let's consider the following tree, which predicts whether a job offer should be accepted. A job offer under consideration begins at the root node, where it is then passed through decision nodes that require choices to be made based on the attributes of the job. These choices split the data across branches that indicate potential outcomes of a decision. They are depicted here as yes or no outcomes, but...
