rf.tune_pam: Tune xgb pam

In adibender/pem.xgb: GBT (PEM)

Description

Tune xgb pam

Usage

rf.tune_pam(
  data,
  param_df,
  nrounds = 1L,
  cv_indices,
  nfold = 4,
  ped_params = list(),
  nthread = 1L,
  early_stopping_rounds = NULL,
  verbose = FALSE,
  print_every_n = 1L,
  ...
)

Arguments

data	training dataset. xgb.train accepts only an xgb.DMatrix as the input. xgboost, in addition, also accepts matrix, dgCMatrix, or name of a local data file.
param_df	A data frame of parameter combinations to tune. One row contains one parameter set that will be passed on to params in xgb.cv_pam.
nrounds	max number of boosting iterations.
nfold	Number of cross-valdation folds.
ped_params	List of parameters used to transform data into PED format.
early_stopping_rounds	If NULL, the early stopping function is not triggered. If set to an integer k, training with a validation set will stop if the performance doesn't improve for k rounds. Setting this parameter engages the cb.early.stop callback.
verbose	If 0, xgboost will stay silent. If 1, it will print information about performance. If 2, some additional information will be printed out. Note that setting verbose > 0 automatically engages the cb.print.evaluation(period=1) callback function.
print_every_n	Print each n-th iteration evaluation messages when verbose>0. Default is 1 which means all messages are printed. This parameter is passed to the cb.print.evaluation callback.
...	other parameters to pass to params.

adibender/pem.xgb documentation built on Sept. 10, 2021, 7:24 p.m.