Ensemble Methods
Decision trees are rarely used alone due to their tendency to overfit. They form the basis of powerful ensemble methods:
- Random Forest: Combines multiple decision trees trained on random subsets
- Gradient Boosting: Sequentially builds trees to correct previous errors
- AdaBoost: Weights samples based on previous mistakes
- Isolation Forest: Uses random tree splits to isolate anomalies (commonly used in Anomaly Detection)