This learner applies a univariate outcome learner across a vector of outcome variables, effectively transforming it into a multivariate outcome learner
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
learnerThe learner to wrap.
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_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
library(data.table)
# simulate data
set.seed(123)
n <- 1000
p <- 5
pY <- 3
W <- matrix(rnorm(n * p), nrow = n)
colnames(W) <- sprintf("W%d", seq_len(p))
Y <- matrix(rnorm(n * pY, 0, 0.2) + W[, 1], nrow = n)
colnames(Y) <- sprintf("Y%d", seq_len(pY))
data <- data.table(W, Y)
covariates <- grep("W", names(data), value = TRUE)
outcomes <- grep("Y", names(data), value = TRUE)
# make sl3 task
task <- sl3_Task$new(data.table::copy(data),
covariates = covariates,
outcome = outcomes
)
# train multivariate learner and make predictions
mv_learner <- make_learner(Lrnr_multivariate, make_learner(Lrnr_glm_fast))
mv_fit <- mv_learner$train(task)
mv_pred <- mv_fit$predict(task)
mv_pred <- unpack_predictions(mv_pred)