# Traditional Credit Scoring Using Logistic Regression
library(scorecard)
# data preparing ------
# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dt_f = var_filter(germancredit, y="creditability")
# breaking dt into train and test
dt_list = split_df(dt_f, y="creditability", ratio = 0.6, seed = 30)
label_list = lapply(dt_list, function(x) x$creditability)
# woe binning ------
bins = woebin(dt_f, y="creditability")
# woebin_plot(bins)
# binning adjustment
## adjust breaks interactively
# breaks_adj = woebin_adj(dt_f, "creditability", bins)
## or specify breaks manually
breaks_adj = list(
age.in.years=c(26, 35, 40),
other.debtors.or.guarantors=c("none", "co-applicant%,%guarantor"))
bins_adj = woebin(dt_f, y="creditability", breaks_list=breaks_adj)
# converting train and test into woe values
dt_woe_list = lapply(dt_list, function(x) woebin_ply(x, bins_adj))
# glm ------
m1 = glm( creditability ~ ., family = binomial(), data = dt_woe_list$train)
# vif(m1, merge_coef = TRUE) # summary(m1)
# Select a formula-based model by AIC (or by LASSO for large dataset)
m_step = step(m1, direction="both", trace = FALSE)
m2 = eval(m_step$call)
# vif(m2, merge_coef = TRUE) # summary(m2)
# # Adjusting for oversampling (support.sas.com/kb/22/601.html)
# library(data.table)
# p1=0.03 # bad probability in population
# r1=0.3 # bad probability in sample dataset
# dt_woe = copy(dt_woe_list$train)[, weight := ifelse(creditability==1, p1/r1, (1-p1)/(1-r1) )][]
# fmla = as.formula(paste("creditability ~", paste(names(coef(m2))[-1], collapse="+")))
# m3 = glm(fmla, family = binomial(), data = dt_woe, weights = weight)
# performance ks & roc ------
## predicted proability
pred_list = lapply(dt_woe_list, function(x) predict(m2, x, type='response'))
## performance
perf = perf_eva(pred = pred_list, label = label_list)
# score ------
## scorecard
card = scorecard(bins_adj, m2)
## credit score
score_list = lapply(dt_list, function(x) scorecard_ply(x, card))
## psi
perf_psi(score = score_list, label = label_list)