Lrnr_lightgbm: LightGBM: Light Gradient Boosting Machine
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters References See Also Examples

This learner provides fitting procedures for lightgbm models, using the lightgbm package, via lgb.train. These gradient boosted decision tree models feature faster training speed and efficiency, lower memory usage than competing frameworks (e.g., from the xgboost package), better prediction accuracy, and improved handling of large-scale data. For details on the fitting procedure and its tuning parameters, consult the documentation of the lightgbm package. The LightGBM framework was introduced in \insertCitelightgbm;textualsl3).

An R6Class object inheriting from Lrnr_base.

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.

num_threads = 1L: Number of threads for hyperthreading.
...: Other arguments passed to lgb.train. See its documentation for further details.

\insertAllCited

Lrnr_gbm for standard gradient boosting models (via the gbm package) and Lrnr_xgboost for the extreme gradient boosted tree models from the Xgboost framework (via the xgboost package).

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_glmnet, Lrnr_glm, Lrnr_grf, Lrnr_gru_keras, Lrnr_gts, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_haldensify, Lrnr_hts, Lrnr_independent_binomial, 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

## Not run: 
# currently disabled since LightGBM crashes R on Windows
# more info at https://github.com/tlverse/sl3/issues/344
data(cpp_imputed)
# create task for prediction
cpp_task <- sl3_Task$new(
  data = cpp_imputed,
  covariates = c("bmi", "parity", "mage", "sexn"),
  outcome = "haz"
)

# initialization, training, and prediction with the defaults
lgb_lrnr <- Lrnr_lightgbm$new()
lgb_fit <- lgb_lrnr$train(cpp_task)
lgb_preds <- lgb_fit$predict()

# get feature importance from fitted model
lgb_varimp <- lgb_fit$importance()

## End(Not run)