R/Lrnr_bartMachine.R
Lrnr_bartMachine.RdThis learner implements Bayesian Additive Regression Trees via
bartMachine (described in Kapelner and Bleich (2016)
)
and the function bartMachine.
A learner object inheriting from Lrnr_base with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base.
...: Parameters passed to bartMachine.
See it's documentation for details.
Kapelner A, Bleich J (2016). “bartMachine: Machine Learning with Bayesian Additive Regression Trees.” Journal of Statistical Software, 70(4), 1--40. doi:10.18637/jss.v070.i04 .
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
Lrnr_arima,
Lrnr_base,
Lrnr_bayesglm,
Lrnr_bilstm,
Lrnr_caret,
Lrnr_cv_selector,
Lrnr_cv,
Lrnr_dbarts,
Lrnr_define_interactions,
Lrnr_density_discretize,
Lrnr_density_hse,
Lrnr_density_semiparametric,
Lrnr_earth,
Lrnr_expSmooth,
Lrnr_gam,
Lrnr_ga,
Lrnr_gbm,
Lrnr_glm_fast,
Lrnr_glm_semiparametric,
Lrnr_glmnet,
Lrnr_glmtree,
Lrnr_glm,
Lrnr_grfcate,
Lrnr_grf,
Lrnr_gru_keras,
Lrnr_gts,
Lrnr_h2o_grid,
Lrnr_hal9001,
Lrnr_haldensify,
Lrnr_hts,
Lrnr_independent_binomial,
Lrnr_lightgbm,
Lrnr_lstm_keras,
Lrnr_mean,
Lrnr_multiple_ts,
Lrnr_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
Lrnr_pca,
Lrnr_pkg_SuperLearner,
Lrnr_polspline,
Lrnr_pooled_hazards,
Lrnr_randomForest,
Lrnr_ranger,
Lrnr_revere_task,
Lrnr_rpart,
Lrnr_rugarch,
Lrnr_screener_augment,
Lrnr_screener_coefs,
Lrnr_screener_correlation,
Lrnr_screener_importance,
Lrnr_sl,
Lrnr_solnp_density,
Lrnr_solnp,
Lrnr_stratified,
Lrnr_subset_covariates,
Lrnr_svm,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
# set up ML task
data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
# fit a bartMachine model and predict from it
bartMachine_learner <- make_learner(Lrnr_bartMachine)
#> Warning: User did not specify Java RAM option, and this learner often fails with the default RAM of 500MB,
#> so setting that now as `options(java.parameters = '-Xmx2500m')`.
#>
#> Note that Xmx parameter's upper limit is system dependent
#> (e.g., 32bit Windows will fail to work with anything much largerthan 1500m),
#> so ideally this option should be specified by the user.
bartMachine_fit <- bartMachine_learner$train(task)
preds <- bartMachine_fit$predict()