Lrnr_bartMachine: BART Machine Learner

Description Usage Format Value Parameters Common Parameters See Also

Description

This learner implements Bayesian Additive Regression Trees, using the bartMachine package.

Usage

1

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

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.

Common Parameters

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

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_base, Lrnr_bilstm, Lrnr_condensier, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_expSmooth, Lrnr_glm_fast, Lrnr_glmnet, Lrnr_glm, Lrnr_grf, 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_stratified, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_xgboost, Pipeline, Stack, define_h2o_X, undocumented_learner


jeremyrcoyle/sl3 documentation built on Oct. 16, 2018, 5:11 p.m.