BARTMachineModel: Bayesian Additive Regression Trees Model

Description

Builds a BART model for regression or classification.

Usage

BARTMachineModel(
  num_trees = 50,
  num_burn = 250,
  num_iter = 1000,
  alpha = 0.95,
  beta = 2,
  k = 2,
  q = 0.9,
  nu = 3,
  mh_prob_steps = c(2.5, 2.5, 4)/9,
  verbose = FALSE,
  ...
)

Value

MLModel class object.

Arguments

num_trees: number of trees to be grown in the sum-of-trees model.
num_burn: number of MCMC samples to be discarded as "burn-in".
num_iter: number of MCMC samples to draw from the posterior distribution.
alpha, beta: base and power hyperparameters in tree prior for whether a node is nonterminal or not.
k: regression prior probability that \(E(Y|X)\) is contained in the interval \((y_{min}, y_{max})\), based on a normal distribution.
q: quantile of the prior on the error variance at which the data-based estimate is placed.
nu: regression degrees of freedom for the inverse \(sigma^2\) prior.
mh_prob_steps: vector of prior probabilities for proposing changes to the tree structures: (GROW, PRUNE, CHANGE).
verbose: logical indicating whether to print progress information about the algorithm.
...: additional arguments to bartMachine.

Details

Response types:: binary factor, numeric
Automatic tuning of grid parameters:: alpha, beta, k, nu

Further model details can be found in the source link below.

In calls to varimp for BARTMachineModel, argument type may be specified as "splits" (default) for the proportion of time each predictor is chosen for a splitting rule or as "trees" for the proportion of times each predictor appears in a tree. Argument num_replicates is also available to control the number of BART replicates used in estimating the inclusion proportions [default: 5]. Variable importance is automatically scaled to range from 0 to 100. To obtain unscaled importance values, set scale = FALSE. See example below.

Examples

Run this code

# \donttest{
## Requires prior installation of suggested package bartMachine to run

model_fit <- fit(sale_amount ~ ., data = ICHomes, model = BARTMachineModel)
varimp(model_fit, method = "model", type = "splits", num_replicates = 20,
       scale = FALSE)
# }