CForestModel: Conditional Random Forest Model

In MachineShop: Machine Learning Models and Tools

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).

Details

Response types:

factor, numeric, Surv

Automatic tuning of grid parameter:

mtry

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

Value

MLModel class object.

See Also

cforest, fit, resample

Examples

fit(sale_amount ~ ., data = ICHomes, model = CForestModel)

MachineShop documentation built on Sept. 18, 2023, 5:06 p.m.