decision-trees

Description: Decision trees split data into subsets based on the value of input features, creating a tree-like model of decisions.

Key Points:

• Easy to interpret and visualize.
• Can handle both numerical and categorical data.
• Prone to overfitting without proper pruning.

Applications: Risk assessment, fraud detection, customer segmentation.