parsnip (version 1.1.0)

ctree_train: A wrapper function for conditional inference tree models

Description

These functions are slightly different APIs for partykit::ctree() and partykit::cforest() that have several important arguments as top-level arguments (as opposed to being specified in partykit::ctree_control()).

Usage

ctree_train(
  formula,
  data,
  weights = NULL,
  minsplit = 20L,
  maxdepth = Inf,
  teststat = "quadratic",
  testtype = "Bonferroni",
  mincriterion = 0.95,
  ...
)
cforest_train(
  formula,
  data,
  weights = NULL,
  minsplit = 20L,
  maxdepth = Inf,
  teststat = "quadratic",
  testtype = "Univariate",
  mincriterion = 0,
  mtry = ceiling(sqrt(ncol(data) - 1)),
  ntree = 500L,
  ...
)

Value

An object of class party (for ctree) or cforest.

Arguments

formula

A symbolic description of the model to be fit.

data

A data frame containing the variables in the model.

weights

A vector of weights whose length is the same as nrow(data). For partykit::ctree() models, these are required to be non-negative integers while for partykit::cforest() they can be non-negative integers or doubles.

minsplit

The minimum sum of weights in a node in order to be considered for splitting.

maxdepth

maximum depth of the tree. The default maxdepth = Inf means that no restrictions are applied to tree sizes.

teststat

A character specifying the type of the test statistic to be applied.

testtype

A character specifying how to compute the distribution of the test statistic.

mincriterion

The value of the test statistic (for testtype == "Teststatistic"), or 1 - p-value (for other values of testtype) that must be exceeded in order to implement a split.

...

Other options to pass to partykit::ctree() or partykit::cforest().

mtry

Number of input variables randomly sampled as candidates at each node for random forest like algorithms. The default mtry = Inf means that no random selection takes place.

ntree

Number of trees to grow in a forest.

Examples

if (rlang::is_installed(c("modeldata", "partykit"))) {
  data(bivariate, package = "modeldata")
  ctree_train(Class ~ ., data = bivariate_train)
  ctree_train(Class ~ ., data = bivariate_train, maxdepth = 1)
}