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)



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


The scorecard generated from the function scorecard.


Logical, default is 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.


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


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


Name of force kept variables, such as id column. Default is NULL.


A data frame in score values

See also


# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dt_sel = var_filter(germancredit, "creditability")
#> [INFO] filtering variables ...
# woe binning ------ bins = woebin(dt_sel, "creditability")
#> [INFO] creating woe binning ...
dt_woe = woebin_ply(dt_sel, bins)
#> [INFO] converting into woe values ...
# glm ------ m = glm(creditability ~ ., family = binomial(), data = dt_woe) # summary(m) # Select a formula-based model by AIC m_step = step(m, direction="both", trace=FALSE) m = eval(m_step$call) # summary(m) # predicted proability # dt_pred = predict(m, type='response', dt_woe) # performace # ks & roc plot # perf_eva(dt_woe$creditability, dt_pred) # scorecard # Example I # creat a scorecard card = scorecard(bins, m) card2 = scorecard2(bins=bins, dt=germancredit, y='creditability', x=c("status.of.existing.checking.account", "", "credit.history", "purpose", "credit.amount", "savings.account.and.bonds", "present.employment.since", "", "", "other.debtors.or.guarantors", "property", "", "other.installment.plans", "housing")) # 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 = F)