Decision Tree

 A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome.

The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree in a recursive manner called recursive partitioning. This flowchart-like structure helps you in decision-making. It's visualization like a flowchart diagram which easily mimics the human level thinking. That is why decision trees are easy to understand and interpret.

Source: https://www.youtube.com/watch?v=PHxYNGo8NcI&list=PLeo1K3hjS3us_ELKYSj_Fth2tIEkdKXvV&index=49

https://www.datacamp.com/tutorial/decision-tree-classification-python

Code: https://github.com/LeelaPrasadG/AILearning/tree/main/ML/5_DecisionTree