scorecard creates a scorecard based on the results from woebin and glm.
scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50, basepoints_eq0 = FALSE)
| bins | Binning information generated from |
|---|---|
| model | A glm model object. |
| points0 | Target points, default 600. |
| odds0 | Target odds, default 1/19. Odds = p/(1-p). |
| pdo | Points to Double the Odds, default 50. |
| basepoints_eq0 | Logical, default is FALSE. If it is TRUE, the basepoints will equally distribute to each variable. |
A scorecard data frame
# load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dt_sel = var_filter(germancredit, "creditability")#> [INFO] filtering variables ...#> [INFO] creating woe binning ...#> [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", "duration.in.month", "credit.history", "purpose", "credit.amount", "savings.account.and.bonds", "present.employment.since", "installment.rate.in.percentage.of.disposable.income", "personal.status.and.sex", "other.debtors.or.guarantors", "property", "age.in.years", "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)