![]() ![]() It is a special type of bagging applied to decision trees.Ĭompared to the standard CART model (Chapter the random forest provides a strong improvement, which consists of applying bagging to the data and bootstrap sampling to the predictor variables at each split (James et al. Read more on bootstrapping in the Chapter Forest algorithm, is one of the most commonly used and the most powerful machine learning techniques. Here, each tree is build independently to the others. It consists of building multiple different decision tree models from a single training data set by repeatedly using multiple bootstrapped subsets of the data and averaging the models. There are different powerful alternatives to the classical CART algorithm, including bagging, Random Forest and boosting.īagging stands for bootstrap aggregating. The output of this strategy is very unstable and the tree structure might be severally affected by a small change in the training data set. ![]() The standard decision tree model, CART for classification and regression trees, build only one single tree, which is then used to predict the outcome of new observations. In the Chapter we have described how to build decision trees for predictive modeling. ![]()
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