Wrapper for bartMachine learner
Support bayesian additive regression trees via the bartMachine package.
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, ...)
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) |
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