get_iv_all: Calculate Information Value (IV) `get_iv` is used to calculate Information Value (IV) of an independent variable. `get_iv_all` can loop through IV for all specified independent variables.

Description

Calculate Information Value (IV) get_iv is used to calculate Information Value (IV) of an independent variable. get_iv_all can loop through IV for all specified independent variables.

Usage

get_iv_all(
  dat,
  x_list = NULL,
  ex_cols = NULL,
  breaks_list = NULL,
  target = NULL,
  pos_flag = NULL,
  best = TRUE,
  equal_bins = FALSE,
  tree_control = NULL,
  bins_control = NULL,
  g = 10,
  parallel = FALSE,
  note = FALSE
)
get_iv(
  dat,
  x,
  target = NULL,
  pos_flag = NULL,
  breaks = NULL,
  breaks_list = NULL,
  best = TRUE,
  equal_bins = FALSE,
  tree_control = NULL,
  bins_control = NULL,
  g = 10,
  note = FALSE
)

Arguments

dat

A data.frame with independent variables and 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.

breaks_list

A table containing a list of splitting points for each independent variable. Default is NULL.

target

The name of target variable.

pos_flag

Value of positive class, Default is "1".

best

Logical, merge initial breaks to get optimal breaks for binning.

equal_bins

Logical, generates initial breaks for equal frequency binning.

tree_control

Parameters of using Decision Tree to segment initial breaks. See detials: get_tree_breaks

bins_control

Parameters used to control binning. See detials: select_best_class, select_best_breaks

Number of initial breakpoints for equal frequency binning.

parallel

Logical, parallel computing. Default is FALSE.

note

Logical, outputs info. Default is TRUE.

The name of an independent variable.

breaks

Splitting points for an independent variable. Default is NULL.

Details

IV Rules of Thumb for evaluating the strength a predictor Less than 0.02:unpredictive 0.02 to 0.1:weak 0.1 to 0.3:medium 0.3 + :strong

References

Information Value Statistic:Bruce Lund, Magnify Analytics Solutions, a Division of Marketing Associates, Detroit, MI(Paper AA - 14 - 2013)

Examples

Run this code

# NOT RUN {
get_iv_all(dat = UCICreditCard,
 x_list = names(UCICreditCard)[3:10],
 equal_bins = TRUE, best = FALSE,
 target = "default.payment.next.month",
 ex_cols = "ID|apply_date")
get_iv(UCICreditCard, x = "PAY_3",
       equal_bins = TRUE, best = FALSE,
 target = "default.payment.next.month")
# }

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