Stratify learner fits by a single variable
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
learner="learner"An initialized Lrnr_* object.
variable_stratify="variable_stratify"character giving
the variable in the covariates on which to stratify. Supports only
variables with discrete levels coded as numeric.
...Other parameters passed directly to
learner$train. See its documentation for details.
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
Lrnr_arima,
Lrnr_bartMachine,
Lrnr_base,
Lrnr_bayesglm,
Lrnr_bilstm,
Lrnr_caret,
Lrnr_cv_selector,
Lrnr_cv,
Lrnr_dbarts,
Lrnr_define_interactions,
Lrnr_density_discretize,
Lrnr_density_hse,
Lrnr_density_semiparametric,
Lrnr_earth,
Lrnr_expSmooth,
Lrnr_gam,
Lrnr_ga,
Lrnr_gbm,
Lrnr_glm_fast,
Lrnr_glm_semiparametric,
Lrnr_glmnet,
Lrnr_glmtree,
Lrnr_glm,
Lrnr_grfcate,
Lrnr_grf,
Lrnr_gru_keras,
Lrnr_gts,
Lrnr_h2o_grid,
Lrnr_hal9001,
Lrnr_haldensify,
Lrnr_hts,
Lrnr_independent_binomial,
Lrnr_lightgbm,
Lrnr_lstm_keras,
Lrnr_mean,
Lrnr_multiple_ts,
Lrnr_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
Lrnr_pca,
Lrnr_pkg_SuperLearner,
Lrnr_polspline,
Lrnr_pooled_hazards,
Lrnr_randomForest,
Lrnr_ranger,
Lrnr_revere_task,
Lrnr_rpart,
Lrnr_rugarch,
Lrnr_screener_augment,
Lrnr_screener_coefs,
Lrnr_screener_correlation,
Lrnr_screener_importance,
Lrnr_sl,
Lrnr_solnp_density,
Lrnr_solnp,
Lrnr_subset_covariates,
Lrnr_svm,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
library(data.table)
# load example data set
data(cpp_imputed)
setDT(cpp_imputed)
# use covariates of intest and the outcome to build a task object
covars <- c("apgar1", "apgar5", "sexn")
task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = "haz")
hal_lrnr <- Lrnr_hal9001$new(fit_control = list(n_folds = 3))
stratified_hal <- Lrnr_stratified$new(
learner = hal_lrnr,
variable_stratify = "sexn"
)
# stratified learner
set.seed(123)
stratified_hal_fit <- stratified_hal$train(task)
stratified_prediction <- stratified_hal_fit$predict(task = task)