This learner uses glmtree from partykit to fit
recursive partitioning and regression trees in a generalized linear model.
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
formula: An optional object of class formula (or one that
can be coerced to that class), which a symbolic description of the
generalized linear model to be fit. If not specified a main terms
regression model will be supplied, with each covariate included as
a term. Please consult glmtree documentation
for more information on its use of formula, and for a
description on formula syntax consult the details of the
glm documentation.
...: Other parameters passed to
mob_control or glmtree
that are not already specified in the sl3_Task. See its
documentation for details.
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_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
data(cpp_imputed)
# create task for prediction
cpp_task <- sl3_Task$new(
data = cpp_imputed,
covariates = c("bmi", "parity", "mage", "sexn"),
outcome = "haz"
)
# initialization, training, and prediction with the defaults
glmtree_lrnr <- Lrnr_glmtree$new()
glmtree_fit <- glmtree_lrnr$train(cpp_task)
glmtree_preds <- glmtree_fit$predict()