radiant.model (version 1.6.7)

cv.crtree: Cross-validation for Classification and Regression Trees

Description

Cross-validation for Classification and Regression Trees

Usage

cv.crtree(
  object,
  K = 5,
  repeats = 1,
  cp,
  pcp = seq(0, 0.01, length.out = 11),
  seed = 1234,
  trace = TRUE,
  fun,
  ...
)

Value

A data.frame sorted by the mean, sd, min, and max of the performance metric

Arguments

object

Object of type "rpart" or "crtree" to use as a starting point for cross validation

K

Number of cross validation passes to use

repeats

Number of times to repeat the K cross-validation steps

cp

Complexity parameter used when building the (e.g., 0.0001)

pcp

Complexity parameter to use for pruning

seed

Random seed to use as the starting point

trace

Print progress

fun

Function to use for model evaluation (e.g., auc for classification or RMSE for regression)

...

Additional arguments to be passed to 'fun'

Details

See https://radiant-rstats.github.io/docs/model/crtree.html for an example in Radiant

See Also

crtree to generate an initial model that can be passed to cv.crtree

Rsq to calculate an R-squared measure for a regression

RMSE to calculate the Root Mean Squared Error for a regression

MAE to calculate the Mean Absolute Error for a regression

auc to calculate the area under the ROC curve for classification

profit to calculate profits for classification at a cost/margin threshold

Examples

if (FALSE) {
result <- crtree(dvd, "buy", c("coupon", "purch", "last"))
cv.crtree(result, cp = 0.0001, pcp = seq(0, 0.01, length.out = 11))
cv.crtree(result, cp = 0.0001, pcp = c(0, 0.001, 0.002), fun = profit, cost = 1, margin = 5)
result <- crtree(diamonds, "price", c("carat", "color", "clarity"), type = "regression", cp = 0.001)
cv.crtree(result, cp = 0.001, pcp = seq(0, 0.01, length.out = 11), fun = MAE)
}