Train XGBoost Model Using the IBLM Model Parameters

Trains an XGBoost model using parameters extracted from the booster residual component of the iblm model. This is a convenient way to fit an XGBoost model for direct comparison with a fitted iblm model

Usage

train_xgb_as_per_iblm(iblm_model, ...)

Arguments

iblm_model

Ensemble model object of class "iblm" containing GLM and XGBoost model components. Also contains data that was used to train it.

...

optional arguments to insert into xgb.train(). Note this will cause deviation from the settings used for training `iblm_model`

Value

Trained XGBoost model object (class "xgb.Booster").

See also

xgb.train

Examples

df_list <- freMTPLmini |> split_into_train_validate_test(seed = 9000)

# training with plenty of rounds allowed
iblm_model1 <- train_iblm_xgb(
  df_list,
  response_var = "ClaimRate",
  family = "poisson",
  max_depth = 6,
  nrounds = 1000
)

xgb1 <- train_xgb_as_per_iblm(iblm_model1)

# training with severe restrictions (expected poorer results)
iblm_model2 <- train_iblm_xgb(
  df_list,
  response_var = "ClaimRate",
  family = "poisson",
  max_depth = 1,
  nrounds = 5
)

xgb2 <- train_xgb_as_per_iblm(iblm_model2)

# comparison shows the poor training mirrored in second set:
get_pinball_scores(
  df_list$test,
  iblm_model1,
  trim = NA_real_,
  additional_models = list(iblm2 = iblm_model2, xgb1 = xgb1, xgb2 = xgb2)
)
#>   model poisson_deviance pinball_score
#> 1 homog         1.313877    0.00000000
#> 2   glm         1.232683    0.06179691
#> 3  iblm         1.187413    0.09625226
#> 4 iblm2         1.211137    0.07819591
#> 5  xgb1         1.216096    0.07442165
#> 6  xgb2         1.660000   -0.26343662