This learner provides fitting procedures for subsetting covariates. It is a convenience utility for reducing the number of covariates to be fit.
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
...Not currently used.
Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared
by all learners.
covariatesA character vector of covariates. The learner will use this to subset the covariates for any specified task
outcome_typeA 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_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_stratified,
Lrnr_svm,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
# load example data
data(cpp_imputed)
covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
outcome <- "haz"
# create sl3 task
task <- sl3_Task$new(data.table::copy(cpp_imputed),
covariates = covars,
outcome = outcome,
folds = origami::make_folds(cpp_imputed, V = 3)
)
glm_learner <- Lrnr_glm$new()
glmnet_learner <- Lrnr_glmnet$new()
subset_apgar <- Lrnr_subset_covariates$new(covariates = c("apgar1", "apgar5"))
learners <- list(glm_learner, glmnet_learner, subset_apgar)
sl <- make_learner(Lrnr_sl, learners, glm_learner)
sl_fit <- sl$train(task)
sl_pred <- sl_fit$predict()