GiniImportanceTree: computes Gini information gain for one tree from randomForest

In rfVarImpOOB: Unbiased Variable Importance for Random Forests

computes Gini information gain for one tree from randomForest

Description

computes importance scores for an individual tree.

These can be based on Gini impurity or Accuracy or logloss

Usage

GiniImportanceTree(bag, RF, k, ylabel = "Survived", returnTree = FALSE, 


    zeroLeaf = TRUE, score = c("PMDI21", "MDI", "MDA", "MIA")[1], 


    Predictor = Mode, verbose = 0)

Arguments

bag	data to compute the Gini gain for
RF	object returned by call to randomForest()
k	which tree
ylabel	name of dependent variable
returnTree	if TRUE returns the tree data frame otherwise the aggregated Gini importance grouped by split variables
zeroLeaf	if TRUE discard the information gain due to splits resulting in n=1
score	scoring method:PMDI=mean decrease penalized Gini impurity (note:the last digit is the exponent of the penalty!), MDI=mean decrease impurity (Gini), MDA=mean decrease accuracy (permutation), MIA=mean increase accuracy
Predictor	function to estimate node prediction, such as Mode or mean or median. Alternatively, pass an array of numbers as replacement for the yHat column of tree
verbose	level of verbosity

Value

if returnTree==TRUE returns the tree data frame otherwise the aggregated Gini importance grouped by split variables

Author(s)

Markus Loecher <[e-mail]>

Examples

rfTit = rfTitanic(nRows = 500,nodesize=10)


rfTit$data$Survived = as.numeric(rfTit$data$Survived)-1


k=1


tmp <- InOutBags(rfTit$RF, rfTit$data, k)


IndivTree =getTree(rfTit$RF,k)


#plot(as.party(tmp))#does not work


InTree = GiniImportanceTree(tmp$inbag,rfTit$RF,k,returnTree=TRUE)


OutTree = GiniImportanceTree(tmp$outbag,rfTit$RF,k,returnTree=TRUE)

rfVarImpOOB documentation built on July 1, 2022, 5:05 p.m.