summarize_tree: Useful summaries of partition models from rpart

In regclass: Tools for an Introductory Class in Regression and Modeling

Useful summaries of partition models from rpart

Description

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

Usage

summarize_tree(TREE)

Arguments

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

Details

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

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

rpart, randomForest

Examples

  data(WINE)
  set.seed(2025); SUBSET <- WINE[sample(1:nrow(WINE),size=500),]
	TREE <- rpart(Quality~.,data=SUBSET,control=rpart.control(cp=0.01,xval=10,minbucket=5))
	summarize_tree(TREE)
	RF <- randomForest(Quality~.,data=SUBSET,ntrees=50)
	summarize_tree(RF)
	
	data(NFL)
	SUBSET <- NFL[,1:10]
	TREE <- rpart(X4.Wins~.,data=SUBSET,control=rpart.control(cp=0.002,xval=10,minbucket=5))
	summarize_tree(TREE)
	RF <- randomForest(X4.Wins~.,data=SUBSET,ntrees=50)
	summarize_tree(RF)
	 

regclass documentation built on June 8, 2025, 12:40 p.m.