GiniImportanceTree {rfVarImpOOB} | R Documentation |
computes importance scores for an individual tree.
These can be based on Gini impurity or Accuracy or logloss
GiniImportanceTree(bag, RF, k, ylabel = "Survived", returnTree = FALSE,
zeroLeaf = TRUE, score = c("PMDI21", "MDI", "MDA", "MIA")[1],
Predictor = Mode, verbose = 0)
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 |
if returnTree==TRUE returns the tree data frame otherwise the aggregated Gini importance grouped by split variables
Markus Loecher <Markus.Loecher@gmail.com>
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)