Description: Random forest regression is an ensemble of decision trees for regression tasks, averaging the predictions to improve accuracy and control overfitting.
Key Points:
- Reduces overfitting compared to individual decision trees.
- Handles large datasets with higher dimensionality.
- Requires more computational resources.
Applications: Environmental modeling, energy demand forecasting, market analysis.