Random Forest - Mathematics and Statistics

This is page 587 Printer: Opaque IntroductionBagging orbootstrap aggregation(section ) is a technique for reducingthe variance of an estimated prediction function. Bagging seems to workespecially well for high-variance, low-bias procedures, such as trees. Forregression, we simply fit the same regression tree many times to bootstrap-sampled versions of the training data, and average the result. For classifi-cation, acommitteeof trees each cast a vote for the predicted in Chapter 10 was initially proposed as a committee method aswell, although unlike bagging, the committee ofweak learnersevolves overtime, and the members cast a weighted vote.

Random Forest Gradient Boosting (5 Node) FIGURE 15.1. Bagging, random forest, and gradient boosting, applied to the spam data. For boosting, 5-node trees were used, and the number of trees were chosen by 10-fold cross-validation (2500 trees). Each “step” in the figure corre-sponds to a change in a single misclassification (in a test set ...

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