summarize.tree: Useful summaries of partition models from rpart
In profpetrie/regclass: Tools for an Introductory Class in Regression and Modeling

Description Usage Arguments Details Author(s) References See Also Examples

Reports the RMSE, AIC, and variable importances for a partition model or the variable importances from a random forest.

1	summarize.tree(TREE)

TREE

A partition model created with rpart or a random forest from randomForest

Extracts the RMSE and AIC of a partition model and the variable importances of partition models or random forests.

Adam Petrie

Introduction to Regression and Modeling

rpart, randomForest

  data(WINE)
	TREE <- rpart(Quality~.,data=WINE,control=rpart.control(cp=0.01,xval=10,minbucket=5))
	summarize.tree(TREE)
	RF <- randomForest(Quality~.,data=WINE)
	summarize.tree(RF)
	
	data(NFL)
	TREE <- rpart(X4.Wins~.,data=NFL,control=rpart.control(cp=0.002,xval=10,minbucket=5))
	summarize.tree(TREE)
	RF <- randomForest(X4.Wins~.,data=NFL,ntrees=500)
	summarize.tree(RF)