This metalearner provides fitting procedures for any pairing of loss or risk
function and metalearner function, subject to constraints. The optimization
problem is solved by making use of the ga function in the
GA R package. For further consult the documentation of this package.
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_function = metalearner_linear: A function(alpha, X) that
takes a vector of covariates and a matrix of data and combines them
into a vector of predictions. See metalearners for
options.
eval_function = loss_squared_error: A function(pred, truth) that
takes prediction and truth vectors and returns a loss vector or a risk
scalar. See loss_functions and
risk_functions for options and more detail.
make_sparse = TRUE: If TRUE, zeros out small alpha values.
convex_combination = TRUE: If TRUE, constrain alpha to sum
to 1.
maxiter = 100: The maximum number of iterations to run before the
GA search is halted.
run = 10: The number of consecutive generations without any
improvement in the best fitness value before the GA is stopped.
optim = TRUE: A logical determining whether or not a local search
using general-purpose optimization algorithms should be used. Argument
optimArgs of ga provides further details and
finer control.
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_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
# define ML task
data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
# build relatively fast learner library (not recommended for real analysis)
lasso_lrnr <- Lrnr_glmnet$new()
glm_lrnr <- Lrnr_glm$new()
ranger_lrnr <- Lrnr_ranger$new()
lrnrs <- c(lasso_lrnr, glm_lrnr, ranger_lrnr)
names(lrnrs) <- c("lasso", "glm", "ranger")
lrnr_stack <- make_learner(Stack, lrnrs)
# instantiate SL with GA metalearner
ga <- Lrnr_ga$new()
sl <- Lrnr_sl$new(lrnr_stack, ga)
sl_fit <- sl$train(task)