Decision trees are interpretable models that make decisions through hierarchical splits.
Key concepts:
- Splitting: Divide data based on feature thresholds
- Information gain: Choose splits that maximize purity
- Pruning: Prevent overfitting by limiting depth
- Leaf nodes: Terminal nodes with class predictions
Each node shows the split condition, colored by majority class.