The data set contains one strong binary predictor, one medium-effect binary predictor and one weak binary predictor. For example, imagine a large data set where classification is to be performed. There is one nuance: executing bagging in a straightforward manner, with no tweaks, may lead to high correlation among the trees. It says: create many bootstrap samples of the data, grow a tree on each of them and average the results. Random forest utilizes the idea of bagging. In the regression setting regression trees are used, while in the classification setting classification trees are used. As such, random forest qualifies as an ensemble method. It combines many decision trees to produce an overall verdict. Random Forest is a machine learning method for regression and classification.
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