The goal of `scorecard`

package is to make the development of traditional credit risk scorecard model easier and efficient by providing functions for some common tasks. - data partition (`split_df`

) - variable selection (`iv`

, `var_filter`

) - weight of evidence (woe) binning (`woebin`

, `woebin_plot`

, `woebin_adj`

, `woebin_ply`

) - scorecard scaling (`scorecard`

, `scorecard_ply`

) - performance evaluation (`perf_eva`

, `perf_psi`

)

- Install the release version of
`scorecard`

from CRAN with:

- Install the latest version of
`scorecard`

from github with:

This is a basic example which shows you how to develop a common credit risk scorecard:

```
# Traditional Credit Scoring Using Logistic Regression
library(scorecard)
# data prepare ------
# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dt_s = var_filter(germancredit, y="creditability")
# breaking dt into train and test
dt_list = split_df(dt_s, y="creditability", ratio = 0.6, seed = 30)
train = dt_list$train; test = dt_list$test;
# woe binning ------
bins = woebin(dt_s, y="creditability")
# woebin_plot(bins)
# binning adjustment
# # adjust breaks interactively
# breaks_adj = woebin_adj(dt_s, "creditability", bins)
# # or specify breaks manually
breaks_adj = list(
age.in.years=c(26, 35, 40),
other.debtors.or.guarantors=c("none", "co-applicant%,%guarantor"))
bins_adj = woebin(dt_s, y="creditability", breaks_list=breaks_adj)
# converting train and test into woe values
train_woe = woebin_ply(train, bins_adj)
test_woe = woebin_ply(test, bins_adj)
# glm ------
m1 = glm( creditability ~ ., family = binomial(), data = train_woe)
# summary(m1)
# # Adjusting for oversampling (support.sas.com/kb/22/601.html)
# library(data.table)
# p1=0.03; r1=0.3
# dt_woe = dt_woe[, weight := ifelse(y==1, p1/r1, (1-p1)/(1-r1) )]
#
# fmla = as.formula(paste("y ~", paste(names(dt_woe)[2:21], collapse="+")))
# m1 = glm(fmla, family = binomial(), data = dt_woe, weights = weight)
# Select a formula-based model by AIC (or by LASSO)
m_step = step(m1, direction="both", trace = FALSE)
m2 = eval(m_step$call)
# summary(m2)
# performance ks & roc ------
# predicted proability
train_pred = predict(m2, train_woe, type='response')
test_pred = predict(m2, test_woe, type='response')
# performance
train_perf = perf_eva(train$creditability, train_pred, title = "train")
test_perf = perf_eva(test$creditability, test_pred, title = "test")
# score ------
card = scorecard(bins_adj, m2)
# credit score
train_score = scorecard_ply(train, card, print_step=0)
test_score = scorecard_ply(test, card, print_step=0)
# psi
perf_psi(
score = list(train = train_score, test = test_score),
label = list(train = train$creditability, test = test$creditability)
)
```