Machine Learning, Uc Berkeley, Random Forest, Decision Tree, Ensemble Learning.
bagging and boosting utilize to create a diverse set of classifiers. In bagging, we create multiple copies of the original training data set using bootstrapping, fit several decision trees to each of the different copies, and take the average of all the predictions of all the trees to make our