A decision tree is one of the most well-known classification techniques. It can be employed for both types of supervised learning problems (classification and regression problems). It is a flowchart-like tree structure and mimics human-level thinking, which makes it easier to understand and interpret. It also makes you see the logic behind the prediction unlike black-box algorithms such as SVMs and neural networks.
The decision tree has three basic components: the internal node, the branch, and leaf nodes. Here, each terminal node represents a feature, the link represents the decision rule or split rule, and the leaf provides the result of the prediction. The first starting or master node in the tree is the root node. It partitions the data based on features or attributes values. Here, we divide the data and again divide the remaining data recursively until all the items refer to the same class or there are no more columns left. Decision trees can be employed...
