Machine Learning, Uc Berkeley, Random Forest, Decision Tree, Ensemble Learning.
scenario of predicting the price of a house. First, we train a decision tree on all the inputs in the training data, which can be treated as a function f1(x)f1(x). This decision tree is bound to have predictions that are slightly off from the actual values, so we figure out the error for each