creditmodel (version 1.3.1)

get_breaks_all: Generates Best Breaks for Binning

Description

get_breaks is for generating optimal binning for numerical and nominal variables. The get_breaks_all is a simpler wrapper for get_breaks.

Usage

get_breaks_all(
  dat,
  target = NULL,
  x_list = NULL,
  ex_cols = NULL,
  pos_flag = NULL,
  occur_time = NULL,
  oot_pct = 0.7,
  best = TRUE,
  equal_bins = FALSE,
  cut_bin = "equal_depth",
  g = 10,
  sp_values = NULL,
  tree_control = list(p = 0.05, cp = 1e-06, xval = 5, maxdepth = 10),
  bins_control = list(bins_num = 10, bins_pct = 0.05, b_chi = 0.05, b_odds = 0.1, b_psi
    = 0.05, b_or = 0.15, mono = 0.3, odds_psi = 0.2, kc = 1),
  parallel = FALSE,
  note = FALSE,
  save_data = FALSE,
  file_name = NULL,
  dir_path = tempdir(),
  ...
)
get_breaks(
  dat,
  x,
  target = NULL,
  pos_flag = NULL,
  best = TRUE,
  equal_bins = FALSE,
  cut_bin = "equal_depth",
  g = 10,
  sp_values = NULL,
  occur_time = NULL,
  oot_pct = 0.7,
  tree_control = NULL,
  bins_control = NULL,
  note = FALSE,
  ...
)

Arguments

dat

A data frame with x and target.

target

The name of target variable.

x_list

A list of x variables.

ex_cols

A list of excluded variables. Default is NULL.

pos_flag

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

occur_time

The name of the variable that represents the time at which each observation takes place.

oot_pct

Percentage of observations retained for overtime test (especially to calculate PSI). Defualt is 0.7

best

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

equal_bins

Logical, if TRUE, equal sample size initial breaks generates.If FALSE , tree breaks generates using desison tree.

cut_bin

A string, if equal_bins is TRUE, 'equal_depth' or 'equal_width', default is 'equal_depth'.

g

Integer, number of initial bins for equal_bins.

sp_values

A list of missing values.

tree_control

the list of tree parameters.

• p the minimum percent of observations in any terminal <leaf> node. 0 < p< 1; 0.01 to 0.1 usually work.

• cp complexity parameter. the larger, the more conservative the algorithm will be. 0 < cp< 1 ; 0.0001 to 0.0000001 usually work.

• xval number of cross-validations.Default: 5

• max_depth maximum depth of a tree. Default: 10

bins_control

the list of parameters.

• bins_num The maximum number of bins. 5 to 10 usually work. Default: 10

• bins_pct The minimum percent of observations in any bins. 0 < bins_pct < 1 , 0.01 to 0.1 usually work. Default: 0.02

• b_chi The minimum threshold of chi-square merge. 0 < b_chi< 1; 0.01 to 0.1 usually work. Default: 0.02

• b_odds The minimum threshold of odds merge. 0 < b_odds < 1; 0.05 to 0.2 usually work. Default: 0.1

• b_psi The maximum threshold of PSI in any bins. 0 < b_psi < 1 ; 0 to 0.1 usually work. Default: 0.05

• b_or The maximum threshold of G/B index in any bins. 0 < b_or < 1 ; 0.05 to 0.3 usually work. Default: 0.15

• odds_psi The maximum threshold of Training and Testing G/B index PSI in any bins. 0 < odds_psi < 1 ; 0.01 to 0.3 usually work. Default: 0.1

• mono Monotonicity of all bins, the larger, the more nonmonotonic the bins will be. 0 < mono < 0.5 ; 0.2 to 0.4 usually work. Default: 0.2

• kc number of cross-validations. 1 to 5 usually work. Default: 1

parallel

Logical, parallel computing or not. Default is FALSE.

note

Logical.Outputs info.Default is TRUE.

save_data

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

file_name

File name that save results in locally specified folder. Default is "breaks_list".

dir_path

Path to save results. Default is "./variable"

...

Additional parameters.

x

The Name of an independent variable.

Value

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

See Also

get_tree_breaks, cut_equal, select_best_class, select_best_breaks

Examples

# NOT RUN {
#controls
tree_control = list(p = 0.02, cp = 0.000001, xval = 5, maxdepth = 10)
bins_control = list(bins_num = 10, bins_pct = 0.02, b_chi = 0.02, b_odds = 0.1,
                   b_psi = 0.05, b_or = 15, mono = 0.2, odds_psi = 0.1, kc = 5)
# get categrory variable breaks
b =  get_breaks(dat = UCICreditCard[1:1000,], x = "MARRIAGE",
                target = "default.payment.next.month",
                occur_time = "apply_date",
                sp_values = list(-1, "missing"),
                tree_control = tree_control, bins_control = bins_control)
# get numeric variable breaks
b2 =  get_breaks(dat = UCICreditCard[1:1000,], x = "PAY_2",
                 target = "default.payment.next.month",
                 occur_time = "apply_date",
                 sp_values = list(-1, "missing"),
                 tree_control = tree_control, bins_control = bins_control)
# get breaks of all predictive variables
b3 =  get_breaks_all(dat = UCICreditCard[1:1000,], target = "default.payment.next.month",
                     x_list = c("MARRIAGE","PAY_2"),
                     occur_time = "apply_date", ex_cols = "ID",
                     sp_values = list(-1, "missing"),
                    tree_control = tree_control, bins_control = bins_control,
                     save_data = FALSE)

# }