Lrnr_xgboost: xgboost: eXtreme Gradient Boosting
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters References See Also Examples

This learner provides fitting procedures for xgboost models, using the xgboost package, via xgb.train. Such models are classification and regression trees with extreme gradient boosting. For details on the fitting procedure, consult the documentation of the xgboost and \insertCitexgboost;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.

nrounds=20: Number of fitting iterations.
...: Other parameters passed to xgb.train.

\insertAllCited

Lrnr_gbm for standard gradient boosting models (via the gbm package) and Lrnr_lightgbm for the faster and more efficient gradient boosted trees from the LightGBM framework (via the lightgbm 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_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, Pipeline, Stack, define_h2o_X(), undocumented_learner

data(mtcars)
mtcars_task <- sl3_Task$new(
  data = mtcars,
  covariates = c(
    "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
    "gear", "carb"
  ),
  outcome = "mpg"
)

# initialization, training, and prediction with the defaults
xgb_lrnr <- Lrnr_xgboost$new()
xgb_fit <- xgb_lrnr$train(mtcars_task)
xgb_preds <- xgb_fit$predict()

# get feature importance from fitted model
xgb_varimp <- xgb_fit$importance()