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.