creditmodel (version 1.0)

gbm_filter: Select Features using GBM

Description

gbm_filter is for selecting important features using GBM.

Usage

gbm_filter(dat, target = NULL, x_list = NULL, ex_cols = NULL,
  pos_flag = NULL, GBM.params = gbm_params(), cores_num = 2,
  vars_name = TRUE, note = FALSE, save_data = FALSE,
  file_name = NULL, dir_path = tempdir(), seed = 46, ...)

Arguments

dat

A data.frame with independent variables and target variable.

target

The name of target variable.

x_list

Names of independent variables.

ex_cols

A list of excluded variables. Regular expressions can also be used to match variable names. Default is NULL.

pos_flag

The value of positive class of target variable, default: "1".

GBM.params

Parameters of GBM.The complete list of parameters is available at: gbm.

cores_num

The number of CPU cores to use.

vars_name

Logical, output a list of filtered variables or table with detailed IV and PSI value of each variable. Default is TRUE.

note

Logical, outputs info. Default is TRUE.

save_data

Logical, save results results in locally specified folder. Default is TRUE

file_name

The name for periodically saved results files. Default is "Featrue_importance_GBDT".

dir_path

The path for periodically saved results files. Default is "./variable".

seed

Random number seed. Default is 46.

...

Other parameters to pass to gbdt_params.

Value

Selected variables.

See Also

psi_iv_filter, xgb_filter, feature_select_wrapper

Examples

# NOT RUN {
GBM.params = gbm_params(n.trees = 2, interaction.depth = 4, shrinkage = 0.1,
                       bag.fraction = 0.5, train.fraction = 0.7,
                       n.minobsinnode = 30,
                     cv.folds = NULL, best_iter = TRUE, method = "cv")

gbm_filter(dat =  UCICreditCard[1:1000,c(8:12,26)],
          target = "default.payment.next.month",
          occur_time = "apply_date",
          GBM.params = GBM.params
          , vars_name = FALSE)

# }