xgb_filter: Select Features using XGB

In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Description

xgb_filter is for selecting important features using xgboost.

Usage

xgb_filter(
  dat_train,
  dat_test = NULL,
  target = NULL,
  pos_flag = NULL,
  x_list = NULL,
  occur_time = NULL,
  ex_cols = NULL,
  xgb_params = list(nrounds = 100, max_depth = 6, eta = 0.1, min_child_weight = 1,
    subsample = 1, colsample_bytree = 1, gamma = 0, scale_pos_weight = 1,
    early_stopping_rounds = 10, objective = "binary:logistic"),
  f_eval = "auc",
  cv_folds = 1,
  cp = NULL,
  seed = 46,
  vars_name = TRUE,
  note = TRUE,
  save_data = FALSE,
  file_name = NULL,
  dir_path = tempdir(),
  ...
)

Arguments

dat_train	A data.frame with independent variables and target variable.
dat_test	A data.frame of test data. Default is NULL.
target	The name of target variable.
pos_flag	The value of positive class of target variable, default: "1".
x_list	Names of independent variables.
occur_time	The name of the variable that represents the time at which each observation takes place.
ex_cols	A list of excluded variables. Regular expressions can also be used to match variable names. Default is NULL.
xgb_params	Parameters of xgboost.The complete list of parameters is available at: http://xgboost.readthedocs.io/en/latest/parameter.html.
f_eval	Custimized evaluation function,"ks" & "auc" are available.
cv_folds	Number of cross-validations. Default: 5.
cp	Threshold of XGB feature's Gain. Default is 1/number of independent variables.
seed	Random number seed. Default is 46.
vars_name	Logical, output a list of filtered variables or table with detailed IV and PSI value of each variable. Default is FALSE.
note	Logical, outputs info. Default is TRUE.
save_data	Logical, save results results in locally specified folder. Default is FALSE.
file_name	The name for periodically saved results files. Default is "Feature_importance_XGB".
dir_path	The path for periodically saved results files. Default is "./variable".
...	Other parameters to pass to xgb_params.

Value

Selected variables.

See Also

psi_iv_filter, gbm_filter, feature_selector

Examples

dat = UCICreditCard[1:1000,c(2,4,8:9,26)]
xgb_params = list(nrounds = 100, max_depth = 6, eta = 0.1,
                                       min_child_weight = 1, subsample = 1,
                                       colsample_bytree = 1, gamma = 0, scale_pos_weight = 1,
                                       early_stopping_rounds = 10,
                                       objective = "binary:logistic")
## Not run: 
xgb_features = xgb_filter(dat_train = dat, dat_test = NULL,
target = "default.payment.next.month", occur_time = "apply_date",f_eval = 'ks',
xgb_params = xgb_params,
cv_folds = 1, ex_cols = "ID$|date$|default.payment.next.month$", vars_name = FALSE)

## End(Not run)

creditmodel documentation built on Jan. 7, 2022, 5:06 p.m.