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 Chen and Guestrin (2016)
).
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.
Chen T, Guestrin C (2016). “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785--794.
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_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,
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()