Random Forest

Random Forest leverages the diversity of decision trees to produce more stable and generalizable results than a single tree. Through a bagging strategy, many trees are trained on different samples and random subsets of features. This can reduce overfitting and yield a strong general-purpose model. It also provides approximate feature importance, which offers an additional interpretability benefit.