How does Random Forest reduce overfitting compared to Decision Trees?

Random Forest reduces overfitting by averaging multiple decorrelated decision trees.

Each tree is trained on a bootstrap sample and uses random feature subsets, reducing correlation between trees. Averaging predictions reduces variance while preserving predictive power.