Lrnr_cv: Fit/Predict a learner with Cross Validation

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

Fit/Predict a learner with Cross Validation

Description

A wrapper around any learner that generates cross-validate predictions

Format

R6Class object.

Value

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

Parameters

learner

The learner to wrap

folds=NULL

An origami folds object. If NULL, folds from the task are used

full_fit=FALSE

If TRUE, also fit the underlying learner on the full data. This can then be accessed with predict_fold(task, fold_number="full")

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_caret, Lrnr_cv_selector, 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

Examples

library(origami)

# load example data
data(cpp_imputed)
covars <- c(
  "apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn"
)
outcome <- "haz"

# create sl3 task
task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
glm_learner <- Lrnr_glm$new()
cv_glm <- Lrnr_cv$new(glm_learner, folds = make_folds(cpp_imputed, V = 10))

# train cv learner
cv_glm_fit <- cv_glm$train(task)
preds <- cv_glm_fit$predict()

tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.