Lrnr_stratified: Stratify learner fits by a single variable

In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Stratify learner fits by a single variable

Description

Stratify learner fits by a single variable

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

learner="learner"

An initialized Lrnr_* object.

variable_stratify="variable_stratify"

character giving the variable in the covariates on which to stratify. Supports only variables with discrete levels coded as numeric.

...

Other parameters passed directly to learner$train. See its documentation for details.

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, 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_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_ts_weights, Lrnr_xgboost, Pipeline, Stack, define_h2o_X(), undocumented_learner

Examples

library(data.table)

# load example data set
data(cpp_imputed)
setDT(cpp_imputed)

# use covariates of intest and the outcome to build a task object
covars <- c("apgar1", "apgar5", "sexn")
task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = "haz")

hal_lrnr <- Lrnr_hal9001$new(fit_control = list(n_folds = 3))
stratified_hal <- Lrnr_stratified$new(
  learner = hal_lrnr,
  variable_stratify = "sexn"
)

# stratified learner
set.seed(123)
stratified_hal_fit <- stratified_hal$train(task)
stratified_prediction <- stratified_hal_fit$predict(task = task)

jeremyrcoyle/sl3 documentation built on April 30, 2024, 10:16 p.m.