*2.3. Decision Tree*

A classification model that recursively divides the datasets into sub-parts is known as a decision tree. There are root nodes, internal nodes in the decision tree, and terminal nodes developed by the subdivision of the tree. Each node was derived from a single parent and could have many child nodes. A decision tree helps in the decision-making process. The context of the decision tree decides the probability of sets. The simple structure of the decision tree has nodes and terminal nodes, which is a supervised approach to classification. Nodes represent the dataset's properties, and their results are displayed by terminal nodes. C4.5 and random forest are examples of algorithms used to implement the decision tree [24].
