This learner implements Bayesian Additive Regression Trees, using the
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
Optional dataframe to predict the outcome.
Optional observation-level weights (supported but not tested).
Optional id to group observations from the same unit (not used currently).
"gaussian" for regression, "binomial" for binary classification.
The number of trees to be grown in the sum-of-trees model.
Number of MCMC samples to be discarded as "burn-in".
Number of MCMC samples to draw from the posterior distribution of f(x).
Base hyperparameter in tree prior for whether a node is nonterminal or not.
Power hyperparameter in tree prior for whether a node is nonterminal or not.
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.
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.
Degrees of freedom for the inverse chi^2 prior. Not used for classification.
Prints information about progress of the algorithm to the screen.
Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by
Lrnr_base, and shared
by all learners.
A character vector of covariates. The learner will use this to subset the covariates for any specified task
variable_type object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified
All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating
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