rmse_by_num_trees: Assess the Out-of-sample RMSE by Number of Trees

In bartMachine: Bayesian Additive Regression Trees

Assess the Out-of-sample RMSE by Number of Trees

Description

Assess out-of-sample RMSE of a BART model for varying numbers of trees in the sum-of-trees model.

Usage

rmse_by_num_trees(
  bart_machine,
  tree_list = c(5, seq(10, 50, 10), 100, 150, 200),
  in_sample = FALSE,
  plot = TRUE,
  holdout_pctg = 0.3,
  num_replicates = 4,
  verbose = TRUE,
  ...
)

Arguments

bart_machine	An object of class “bartMachine”.
tree_list	List of sizes for the sum-of-trees models.
in_sample	If TRUE, the RMSE is computed on in-sample data rather than an out-of-sample holdout.
plot	If TRUE, a plot of the RMSE by the number of trees in the ensemble is created.
holdout_pctg	Percentage of the data to be treated as an out-of-sample holdout.
num_replicates	Number of replicates to average the results over. Each replicate uses a randomly sampled holdout of the data, (which could have overlap).
verbose	If TRUE, prints progress messages and plots to the active device.
...	Other arguments to be passed to the plot function.

Value

Invisibly, returns the out-of-sample average RMSEs for each tree size.

Note

Since using a large number of trees can substantially increase computation time, this plot can help assess whether a smaller ensemble size is sufficient to obtain desirable predictive performance. This function is parallelized by the number of cores set in set_bart_machine_num_cores.

Author(s)

Adam Kapelner and Justin Bleich

Examples

## Not run: 
#generate Friedman data
set.seed(11)
n  = 200
p = 10
X = data.frame(matrix(runif(n * p), ncol = p))
y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n)

##build BART regression model
bart_machine = bartMachine(X, y, num_trees = 20)

#explore RMSE by number of trees
rmse_by_num_trees(bart_machine)

## End(Not run)

bartMachine documentation built on Jan. 19, 2026, 9:06 a.m.