Decision Trees

Decision Trees are powerful algorithms used for both classification and regression tasks. They work by recursively splitting the dataset into subsets based on the most significant features, forming a tree-like structure with nodes and branches.

Key components of a decision tree include:

• Root Node: Represents the entire dataset.
• Decision Nodes: Intermediate nodes where further splits occur.
• Leaf Nodes: Represent the final output or decision.

Decision Trees are easy to interpret and visualize, making them popular for applications like credit scoring, medical diagnoses, and fraud detection.