scorecard_ply calculates credit score using the results from scorecard.

scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L,
  replace_blank_na = TRUE, var_kp = NULL)

Arguments

dt

A data frame, which is the original dataset for training model.

card

A data frame or a list of data frames. It's the scorecard generated from the function scorecard.

only_total_score

Logical, Defaults to TRUE. If it is TRUE, then the output includes only total credit score; Otherwise, if it is FALSE, the output includes both total and each variable's credit score.

print_step

A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0, no message is print.

replace_blank_na

Logical. Replace blank values with NA. Defaults to TRUE. This argument should be the same with woebin's.

var_kp

Name of force kept variables, such as id column. Defaults to NULL.

Value

A data frame in score values

Examples

# \donttest{
# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dtvf = var_filter(germancredit, "creditability")
#>  Filtering variables via missing_rate, identical_rate, info_value ...
#>  1 variables are removed via identical_rate
#>  6 variables are removed via info_value
#>  Variable filtering on 1000 rows and 20 columns in 00:00:00
#>  7 variables are removed in total
# split into train and test
dtlst = split_df(dtvf, y = 'creditability')
# binning
bins = woebin(dtlst$train, "creditability")
#>  Creating woe binning ...
#>  Binning on 681 rows and 14 columns in 00:00:01
# scorecard
card = scorecard2(bins=bins, dt=dtlst$train, y='creditability')

# credit score
# Example I # only total score
score1 = scorecard_ply(germancredit, card)

# Example II # credit score for both total and each variable
score2 = scorecard_ply(germancredit, card, only_total_score = FALSE)
# }