fit_xgb.train: Fit boosted regression tree/gradient boosted machine

In dahcase/mbgstacking: Stacked generalization ensemble model for model-based geostatistics

Description

Fit a boosted regression tree (or random forest given the proper set of options). See ??xgboost::xgb.train for more details

Usage

fit_xgb.train(st, model_name = "brt", fold_col = NULL, fold_id = NULL,
  return_model_obj = F, sub_cores = 1)

Arguments

st	stacker governor. Stacking governer object with a penalized model initialized
model_name	character vector. Name of the model to be run
fold_col	character vector. Denotes the name of the column designating the fold for crossval
fold_id	Numeric. Designates the value in fold col that should be held out
return_model_obj	logical. Denotes whether the function should return the earth object or just predictions.
sub_cores	numeric. Number of cores/processes to be used by xgboost.

Value

List object with a data.table of predictions. If return_model_obj==T, the gam command and model object are returned as well

dahcase/mbgstacking documentation built on May 20, 2019, 4:08 p.m.