This learner screens covariates based on their variable importance, where the
importance values are obtained from the learner. Any learner with an
importance method can be used. The set of learners with support for
importance can be found with sl3_list_learners("importance").
Like all other screeners, this learner is intended for use in a
Pipeline, so the output from this learner (i.e., the selected
covariates) can be used as input for the next learner in the pipeline.
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.
learner: An instantiated learner that supports variable importance.
The set of learners with this support can be obtained via
sl3_list_learners("importance").
num_screen = 5: The top n number of "most impotant" variables to
retain.
...: Other parameters passed to the learner's
importance function.
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_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
data(mtcars)
mtcars_task <- sl3_Task$new(
data = mtcars,
covariates = c(
"cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
"gear", "carb"
),
outcome = "mpg"
)
glm_lrnr <- make_learner(Lrnr_glm)
# screening based on \code{\link{Lrnr_ranger}} variable importance
ranger_lrnr_importance <- Lrnr_ranger$new(importance = "impurity_corrected")
ranger_importance_screener <- Lrnr_screener_importance$new(
learner = ranger_lrnr_importance, num_screen = 3
)
ranger_screen_glm_pipe <- Pipeline$new(ranger_importance_screener, glm_lrnr)
ranger_screen_glm_pipe_fit <- ranger_screen_glm_pipe$train(mtcars_task)
# screening based on \code{\link{Lrnr_randomForest}} variable importance
rf_lrnr <- Lrnr_randomForest$new()
rf_importance_screener <- Lrnr_screener_importance$new(
learner = rf_lrnr, num_screen = 3
)
rf_screen_glm_pipe <- Pipeline$new(rf_importance_screener, glm_lrnr)
rf_screen_glm_pipe_fit <- rf_screen_glm_pipe$train(mtcars_task)
# screening based on \code{\link{Lrnr_randomForest}} variable importance
xgb_lrnr <- Lrnr_xgboost$new()
xgb_importance_screener <- Lrnr_screener_importance$new(
learner = xgb_lrnr, num_screen = 3
)
xgb_screen_glm_pipe <- Pipeline$new(xgb_importance_screener, glm_lrnr)
xgb_screen_glm_pipe_fit <- xgb_screen_glm_pipe$train(mtcars_task)