An ML engineer is explaining to a CISO why a 'random forest' model used for network intrusion detection provides better performance than a single decision tree. Which explanation is most accurate?

• A.Random forest uses a different mathematical formula than a decision tree, making it inherently more accurate
• B.Random forest builds many decision trees on random subsets of training data and features, then aggregates their predictions - this reduces overfitting and variance because each tree's individual errors are largely uncorrelated, and averaging uncorrelated errors reduces the overall error rate
• C.Random forest processes data faster than a single decision tree because it distributes computation across multiple trees in parallel
• D.Random forest is always more accurate than a single tree for any dataset and any machine learning task