setup_CART: Setup CART Hyperparameters

In rtemis: Schema-Driven Machine Learning and Visualization

Setup CART Hyperparameters

Description

Setup hyperparameters for CART training.

Usage

setup_CART(
  cp = 0.01,
  maxdepth = 20L,
  minsplit = 2L,
  minbucket = 1L,
  prune_cp = NULL,
  method = "auto",
  model = TRUE,
  maxcompete = 4L,
  maxsurrogate = 5L,
  usesurrogate = 2L,
  surrogatestyle = 0L,
  xval = 0L,
  cost = NULL,
  ifw = FALSE
)

Arguments

cp	(Tunable) Numeric [0, Inf): Complexity parameter.
maxdepth	(Tunable) Integer [1, 30]: Maximum depth of tree.
minsplit	(Tunable) Integer [1, Inf): Minimum number of observations in a node to split.
minbucket	(Tunable) Integer [1, Inf): Minimum number of observations in a terminal node.
prune_cp	(Tunable) Optional Numeric [0, Inf): Complexity for cost-complexity pruning after tree is built. NULL prunes nothing after the tree is built.
method	Character {"auto", "anova", "class", "poisson", "exp"}: Splitting method.
model	Logical: If TRUE, keep a copy of the model frame.
maxcompete	Integer [0, Inf): Maximum number of competitive splits.
maxsurrogate	Integer [0, Inf): Maximum number of surrogate splits.
usesurrogate	Integer [0, 2]: Number of surrogate splits to use.
surrogatestyle	Integer [0, 1]: Type of surrogate splits.
xval	Integer [0, Inf): Number of cross-validation folds.
cost	Optional Numeric (0, Inf) vector: One for each feature. NULL uses 1 for every feature.
ifw	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from rpart::rpart and rpart::rpart.control.

Value

CARTHyperparameters object.

Author(s)

EDG

Examples

cart_hyperparams <- setup_CART(cp = 0.01, maxdepth = 10L, ifw = TRUE)
cart_hyperparams

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.