Bagging and Random Forests
Chapter 3, Bagging, and Chapter 4, Random Forests, demonstrate how to improve the stability and accuracy of the basic decision tree. In this section, we will primarily use the decision tree as base learners and create an ensemble of trees in the same way that we did in Chapter 3, Bagging, and Chapter 4, Random Forests.
The split function is the primary difference between bagging and random forest algorithms for classification and regression trees. Thus, unsurprisingly, we can continue to use the same functions and packages for the regression problem as the counterparts that were used in the classification problem. We will first use the bagging function from the ipred package to set up the bagging algorithm for the housing data:
> housing_bagging <- bagging(formula = HT_Formula,data=ht_imp,nbagg=500, + coob=TRUE,keepX=TRUE) > housing_bagging$err [1] 35820
The trees in the bagging object can be saved to a PDF file in the same way...
