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 the basis on which a decision is made; each branch of a node represents how a choice may lead to the next nodes; and finally, each terminal node, the leaf, represents the outcome produced.
For example, we have just made a couple of decisions that brought us to the point of using a decision tree to solve our advertising problem:
The first condition, or the root is whether the feature type is numerical or categorical. Ad click stream data contain mostly categorical features, so it goes to the right branch. In the next node, our work needs to be interpretable by non-technical clients. So, it goes to the right branch and reaches...
