I am relatively new to R and therefore sorry in advance if this question is weirdly phrased or unclear.
I build an xgboost model based on the tidymodels framework as demonstrated by Julia in her youtube video about the topic (Tuning XGBoost using tidymodels - YouTube).
The problem I now run into is that I would like to save the model for later use (so I can load it into without building the model again), although saving it with the saveRDS function might cause compatibility issues in case of package version updates in the future. A more robust option then seems to be "xgb.save" or "xgb.save.raw", although to utilize this function the model should be of class xgb.booster, while my model is of class workflow. Is there a way to convert a workflow to xgb.booster model within the framework of tidymodels that I'm using or should I adress this problem from another angle and try to find a better way to save workflows instead?
I would appreciate a response of any kind and hopefulle clearly explained the problem.
Below Is some of my code to clarify:
xgb_bundes <- boost_tree( trees = tune(), tree_depth = tune(), sample_size = tune(), mtry = tune(), learn_rate = 0.06 ) %>% set_engine("xgboost") %>% set_mode("classification") xgb_grid <- grid_latin_hypercube( trees(), tree_depth(), sample_size = sample_prop(), finalize(mtry(), data_train), size = 30 ) xgb_wf_bundes <- workflow() %>% add_formula(over_under ~ .) %>% add_model(xgb_bundes) set.seed(210) data_fold_cv<- vfold_cv(data_train, v = 10, strata = over_under) library(doParallel) cores<-detectCores() cl <- makeCluster(cores-1) #Register cluster registerDoParallel(cl) set.seed(285) xgb_bundes_result <- tune_grid( xgb_wf_bundes, resamples = data_fold_cv, grid = xgb_grid, control = control_grid(save_pred = T) ) metrics_results <- xgb_bundes_result %>% collect_metrics() best <- metrics_results %>% filter(.metric == "accuracy") final_xgb_bundes <- finalize_workflow(xgb_wf_bundes, best) model_bundes <- final_xgb_bundes %>% fit(data_train) class(model_bundes)  "workflow" xgb.save(model_bundes, "model bundes") ---Which gives the following error: Error in xgb.save(model_bundes, "model bundes") : model must be xgb.Booster.