Random Forest

Key takeaways

• Random Forest is a powerful ensemble learning method used for classification and regression tasks.
• It builds multiple decision trees and merges their outputs for improved accuracy.
• The technique reduces the risk of overfitting by averaging predictions from various trees.
• Random Forest is robust to noise and can handle large datasets effectively.
• It provides insights into feature importance, aiding in model interpretability.