This learner implements Bayesian Additive Regression Trees, using the
bartMachine
packahe
Lrnr_bartMachine
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
Y
Outcome variable.
X
Covariate dataframe.
newX
Optional dataframe to predict the outcome.
obsWeights
Optional observation-level weights (supported but not tested).
id
Optional id to group observations from the same unit (not used currently).
family
"gaussian" for regression, "binomial" for binary classification.
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".
num_iterations_after_burn_in
Number of MCMC samples to draw from the posterior distribution of f(x).
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.
verbose
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.
covariates
A character vector of covariates. The learner will use this to subset the covariates for any specified task
outcome_type
A 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
Other Learners: Custom_chain
,
Lrnr_HarmonicReg
, Lrnr_arima
,
Lrnr_base
, Lrnr_bilstm
,
Lrnr_condensier
, Lrnr_cv
,
Lrnr_define_interactions
,
Lrnr_expSmooth
,
Lrnr_glm_fast
, Lrnr_glmnet
,
Lrnr_glm
, Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_independent_binomial
,
Lrnr_lstm
, Lrnr_mean
,
Lrnr_nnls
, Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_randomForest
,
Lrnr_ranger
, Lrnr_rpart
,
Lrnr_rugarch
, Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_subset_covariates
,
Lrnr_svm
, Lrnr_tsDyn
,
Lrnr_xgboost
, Pipeline
,
Stack
, define_h2o_X
,
undocumented_learner