MachineShop: CForestModel – R documentation

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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`).

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.

Value

MLModel class object.

Examples

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

MachineShop

Machine Learning Models and Tools

v2.8.0

GPL-3

Authors

Brian J Smith [aut, cre]

Initial release

2021-04-16