What are the main advantages of using a random forest versus a single decision tree? The bootstrapping and aggregation components of random forests lead to a lower variance model when compared to a single decision tree. Random forests are easier to interpret than single decision trees. An ensemble of decision trees will always have lower bias than a single decision tree. Random forests tend to generalize better, whereas single trees are more prone to overfitting All of the above