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 Ke et al. (2017)
).
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.
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T (2017). “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” In Advances in Neural Information Processing Systems, volume 30, 3146--3154.
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_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_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
if (FALSE) {
# 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()
}