This learner is the cross-validated (CV) selector, and it is intended
for use as the metalearner in Lrnr_sl.
Lrnr_cv_selector selects the candidate with the best CV
predictive performance (i.e., lowest CV risk). Specifically,
it aims to optimize the CV risk, and it is defined by a constrained
weighted combination: the weights can either be zero or one, and they
must sum to one. Lrnr_cv_selector optimizes the CV
predictive performance under these constraints by assigning the
candidate with the best CV predictive performance a weight of one
and all others a weight of zero. Thus, Lrnr_cv_selector
and its predictions will be identical to the best-performing
candidate learner and its predictions; this is why we say
Lrnr_cv_selector "selects" the candidate with the best
CV predictive performance.
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.
eval_function = loss_squared_error: A function that takes as input
a vector of predicted values as its first argument and a vector of
observed outcome values as its second argument, and then returns a
vector of losses or a numeric risk. See loss_functions and
risk_functions for options.
folds = NULL: Optional origami-structured cross-validation
folds from the task for training Lrnr_sl, e.g.,
task$folds. This argument is only required and utilized
when eval_function is not a loss function, since the risk
has to be calculated on each validation set separately and then
averaged across them in order to estimate the cross-validated risk.
This argument is ignored when eval_function is a loss.
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
Lrnr_arima,
Lrnr_bartMachine,
Lrnr_base,
Lrnr_bayesglm,
Lrnr_bilstm,
Lrnr_caret,
Lrnr_cv,
Lrnr_dbarts,
Lrnr_define_interactions,
Lrnr_density_discretize,
Lrnr_density_hse,
Lrnr_density_semiparametric,
Lrnr_earth,
Lrnr_expSmooth,
Lrnr_gam,
Lrnr_ga,
Lrnr_gbm,
Lrnr_glm_fast,
Lrnr_glm_semiparametric,
Lrnr_glmnet,
Lrnr_glmtree,
Lrnr_glm,
Lrnr_grfcate,
Lrnr_grf,
Lrnr_gru_keras,
Lrnr_gts,
Lrnr_h2o_grid,
Lrnr_hal9001,
Lrnr_haldensify,
Lrnr_hts,
Lrnr_independent_binomial,
Lrnr_lightgbm,
Lrnr_lstm_keras,
Lrnr_mean,
Lrnr_multiple_ts,
Lrnr_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
Lrnr_pca,
Lrnr_pkg_SuperLearner,
Lrnr_polspline,
Lrnr_pooled_hazards,
Lrnr_randomForest,
Lrnr_ranger,
Lrnr_revere_task,
Lrnr_rpart,
Lrnr_rugarch,
Lrnr_screener_augment,
Lrnr_screener_coefs,
Lrnr_screener_correlation,
Lrnr_screener_importance,
Lrnr_sl,
Lrnr_solnp_density,
Lrnr_solnp,
Lrnr_stratified,
Lrnr_subset_covariates,
Lrnr_svm,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
if (FALSE) {
data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
hal_lrnr <- Lrnr_hal9001$new(
max_degree = 1, num_knots = c(20, 10), smoothness_orders = 0
)
lasso_lrnr <- Lrnr_glmnet$new()
glm_lrnr <- Lrnr_glm$new()
ranger_lrnr <- Lrnr_ranger$new()
lrnrs <- c(hal_lrnr, lasso_lrnr, glm_lrnr, ranger_lrnr)
names(lrnrs) <- c("hal", "lasso", "glm", "ranger")
lrnr_stack <- make_learner(Stack, lrnrs)
metalrnr_discrete_MSE <- Lrnr_cv_selector$new(loss_squared_error)
discrete_sl <- Lrnr_sl$new(
learners = lrnr_stack, metalearner = metalrnr_discrete_MSE
)
discrete_sl_fit <- discrete_sl$train(task)
discrete_sl_fit$cv_risk(loss_squared_error)
}