A decision tree is a tree-like graph, a sequential diagram illustrating all of the possible decision alternatives and the corresponding outcomes. Starting from the root of a tree, every internal node represents what a decision is made based on; each branch of a node represents how a choice may lead to the next nodes; and finally, each terminal node, the leaf, represents an outcome yielded.
For example, we have just made a couple of decisions that brought us to the action of learning decision tree to solve our advertising problem:
The decision tree classifier operates in the form of a decision tree. It maps observations to class assignments (symbolized as leaf nodes), through a series of tests (represented as internal nodes) based on feature values and corresponding conditions (represented as branches). In each node, a question regarding the values and characteristics of a feature is asked...
