MachineShop (version 2.8.0)

CForestModel: Conditional Random Forest Model

Description

An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners.

Usage

CForestModel(
  teststat = c("quad", "max"),
  testtype = c("Univariate", "Teststatistic", "Bonferroni", "MonteCarlo"),
  mincriterion = 0,
  ntree = 500,
  mtry = 5,
  replace = TRUE,
  fraction = 0.632
)

Arguments

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 replace = FALSE).

Value

MLModel class object.

Details

Response Types:

factor, numeric, Surv

Automatic Tuning of Grid Parameters:

mtry

Supplied arguments are passed to cforest_control. Further model details can be found in the source link below.

See Also

cforest, fit, resample

Examples

# NOT RUN {
fit(sale_amount ~ ., data = ICHomes, model = CForestModel)

# }