View source: R/ML_CForestModel.R
CForestModel | R Documentation |
An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners.
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.
cforest
, fit
,
resample
fit(sale_amount ~ ., data = ICHomes, model = CForestModel)
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