![]() Each parent node is split into two child nodes, and the association is denoted by dotted lines. The splitting process begins with a root node at the top and ends with leaf nodes at the bottom. The text on the second line shows the sample size in that node. The number on the first line of the box is the mean of the outcome of all the observations in that node (if it is a leaf node, the mean is used for prediction) and the higher the mean, the greener the box. Every round square box represents a node, and the number at the top of it denotes the ID of the node. The unpruned tree is plotted in Figure 2. The final model can be visualized using the fancyRpartPlot() function 2: fancyRpartPlot(model$finalModel, sub = NULL) Figure 2 Therefore, the final model is the unpruned tree: model$finalModel #> n= 100 #> The final value used for the model was cp = 0.Īs is indicated by the root mean squared error (RMSE), the pruned trees (with cp = 0.001 or cp = 0.01) actually yields worse predictions than the unpruned tree (with cp = 0) 1. #> RMSE was used to select the optimal model using the smallest value. #> Resampling results across tuning parameters: #> Resampling: Leave-One-Out Cross-Validation Here’s the result of the cross-validation process: model #> CART TrControl = trainControl(method = "LOOCV"), model <- train(y ~ x1 + x2, method = "rpart", data = dat, In this example, the trees are trained with minsplit = 20, minbucket = 7 and maxdepth = 30, which are the defaults. maxdepth: the maximum depth of any node of the final tree, with the root node counted as depth 0.minbucket: the minimum number of observations in any terminal node.minsplit: the minimum number of observations that must exist in a node in order for a split to be attempted.This parameter should be set using the rpart::ntrol() function, and the following settings may be of particular interest since they determine when to stop splitting. In this example, the control parameter is, as the name indicates, control. ) of the caret::train() function can capture additional arguments, which are then passed to the workhorse function to control its behavior. There are some other parameters worth mentioning. In this example, cost complexity pruning (with hyperparameter cp = c(0, 0.001, 0.01)) is performed using leave-one-out cross validation. Behind the scenes, the caret::train() function calls the rpart::rpart() function to perform the learning process. ![]() ![]() Using the simulated data as a training set, a CART regression tree can be trained using the caret::train() function with method = "rpart".
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