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SuperLearner (version 2.0-24)

SL.bartMachine: 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)