radiant.model (version 1.6.7)

cv.rforest: Cross-validation for a Random Forest

Description

Cross-validation for a Random Forest

Usage

cv.rforest(
  object,
  K = 5,
  repeats = 1,
  mtry = 1:5,
  num.trees = NULL,
  min.node.size = 1,
  sample.fraction = NA,
  trace = TRUE,
  seed = 1234,
  fun,
  ...
)

Value

A data.frame sorted by the mean of the performance metric

Arguments

object

Object of type "rforest" or "ranger"

K

Number of cross validation passes to use

repeats

Repeated cross validation

mtry

Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables

num.trees

Number of trees to create

min.node.size

Minimal node size

sample.fraction

Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement

trace

Print progress

seed

Random seed to use as the starting point

fun

Function to use for model evaluation (i.e., auc for classification and RMSE for regression)

...

Additional arguments to be passed to 'fun'

Details

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

See Also

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

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 <- rforest(dvd, "buy", c("coupon", "purch", "last"))
cv.rforest(
  result,
  mtry = 1:3, min.node.size = seq(1, 10, 5),
  num.trees = c(100, 200), sample.fraction = 0.632
)
result <- rforest(titanic, "survived", c("pclass", "sex"), max.depth = 1)
cv.rforest(result, mtry = 1:3, min.node.size = seq(1, 10, 5))
cv.rforest(result, mtry = 1:3, num.trees = c(100, 200), fun = profit, cost = 1, margin = 5)
result <- rforest(diamonds, "price", c("carat", "color", "clarity"), type = "regression")
cv.rforest(result, mtry = 1:3, min.node.size = 1)
cv.rforest(result, mtry = 1:3, min.node.size = 1, fun = Rsq)
}