SL.bartMachine: Wrapper for bartMachine learner

In SuperLearner: Super Learner Prediction

Wrapper for bartMachine learner

Description

Support bayesian additive regression trees via the bartMachine package.

Usage

SL.bartMachine(Y, X, newX, family, obsWeights, id, num_trees = 50,
  num_burn_in = 250, verbose = F, alpha = 0.95, beta = 2, k = 2,
  q = 0.9, nu = 3, num_iterations_after_burn_in = 1000, ...)

Arguments

Y	Outcome variable
X	Covariate dataframe
newX	Optional dataframe to predict the outcome
family	"gaussian" for regression, "binomial" for binary classification
obsWeights	Optional observation-level weights (supported but not tested)
id	Optional id to group observations from the same unit (not used currently).
num_trees	The number of trees to be grown in the sum-of-trees model.
num_burn_in	Number of MCMC samples to be discarded as "burn-in".
verbose	Prints information about progress of the algorithm to the screen.
alpha	Base hyperparameter in tree prior for whether a node is nonterminal or not.
beta	Power hyperparameter in tree prior for whether a node is nonterminal or not.
k	For regression, k determines the prior probability that E(Y|X) is contained in the interval (y_min, y_max), based on a normal distribution. For example, when k=2, the prior probability is 95%. For classification, k determines the prior probability that E(Y|X) is between (-3,3). Note that a larger value of k results in more shrinkage and a more conservative fit.
q	Quantile of the prior on the error variance at which the data-based estimate is placed. Note that the larger the value of q, the more aggressive the fit as you are placing more prior weight on values lower than the data-based estimate. Not used for classification.
nu	Degrees of freedom for the inverse chi^2 prior. Not used for classification.
num_iterations_after_burn_in	Number of MCMC samples to draw from the posterior distribution of f(x).
...	Additional arguments (not used)

SuperLearner documentation built on July 26, 2023, 6:05 p.m.