Description Usage Arguments Details Value See Also Examples
View source: R/ML_CForestModel.R
An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners.
1 2 3 4 5 6 7 8 9  CForestModel(
teststat = c("quad", "max"),
testtype = c("Univariate", "Teststatistic", "Bonferroni", "MonteCarlo"),
mincriterion = 0,
ntree = 500,
mtry = 5,
replace = TRUE,
fraction = 0.632
)

teststat 
character specifying the type of the test statistic to be applied. 
testtype 
character specifying how to compute the distribution of the test statistic. 
mincriterion 
value of the test statistic that must be exceeded in order to implement a split. 
ntree 
number of trees to grow in a forest. 
mtry 
number of input variables randomly sampled as candidates at each node for random forest like algorithms. 
replace 
logical indicating whether sampling of observations is done with or without replacement. 
fraction 
fraction of number of observations to draw without
replacement (only relevant if 
factor
, numeric
, Surv
mtry
Supplied arguments are passed to cforest_control
.
Further model details can be found in the source link below.
MLModel
class object.
1  fit(sale_amount ~ ., data = ICHomes, model = CForestModel)

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