party: A laboratory for recursive partitioning, provides details. The party package provides nonparametric regression trees for nominal, ordinal, numeric, censored, and multivariate responses. It turns out that this produces the same tree as the original. Title = "Pruned Regression Tree for Mileage") Main="Pruned Regression Tree for Mileage") # create attractive postcript plot of tree Rsq.rpart(fit) # visualize cross-validation results Par(mfrow=c(1,2)) # two plots on one page The data frame is cu.summary.įit <- rpart(Mileage~Price + Country + Reliability + Type, In this example we will predict car mileage from price, country, reliability, and car type. Title = "Pruned Classification Tree for Kyphosis") Main="Pruned Classification Tree for Kyphosis") Pfit<- prune(fit, cp= fit$cptable),"CP"]) Title = "Classification Tree for Kyphosis") # create attractive postscript plot of tree Summary(fit) # detailed summary of splits Plotcp(fit) # visualize cross-validation results Let's use the data frame kyphosis to predict a type of deformation (kyphosis) after surgery, from age in months (Age), number of vertebrae involved (Number), and the highest vertebrae operated on (Start).įit <- rpart(Kyphosis ~ Age + Number + Start, To automatically select the complexity parameter associated with the smallest cross-validated error.
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Alternatively, you can use the code fragmentįit$cptable),"CP"] Specifically, use printcp( ) to examine the cross-validated error results, select the complexity parameter associated with minimum error, and place it into the prune( ) function. Typically, you will want to select a tree size that minimizes the cross-validated error, the xerror column printed by printcp( ). Prune back the tree to avoid overfitting the data. In trees created by rpart( ), move to the LEFT branch when the stated condition is true (see the graphs below). labels are only appropriate for the "anova" method.ĭetailed results including surrogate splits Plot approximate R-squared and relative error for different splits (2 plots). The following functions help us to examine the results. For example, control=ntrol(minsplit=30, cp=0.001) requires that the minimum number of observations in a node be 30 before attempting a split and that a split must decrease the overall lack of fit by a factor of 0.001 (cost complexity factor) before being attempted. Optional parameters for controlling tree growth. Outcome ~ predictor1+ predictor2+ predictor3+ect. Rpart( formula, data=, method=,control=) where The general steps are provided below followed by two examples. Detailed information on rpart is available in An Introduction to Recursive Partitioning CART Modeling via rpartĬlassification and regression trees (as described by Brieman, Freidman, Olshen, and Stone) can be generated through the rpart package.
This section briefly describes CART modeling, conditional inference trees, and random forests. It helps us explore the stucture of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. Recursive partitioning is a fundamental tool in data mining.
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