Installation

install.packages("scorecard")

# install.packages("devtools")devtools::install_github("shichenxie/scorecard")

Example

# Traditional Credit Scoring Using Logistic Regressionlibrary(scorecard)# data preparing ------# load germancredit datadata("germancredit")# filter variable via missing rate, iv, identical value ratedt_f= var_filter(germancredit,y="creditability")# breaking dt into train and testdt_list= split_df(dt_f,y="creditability",ratios= c(0.6,0.4),seed=30)label_list= lapply(dt_list,function(x)x$creditability)# woe binning ------bins= woebin(dt_f,y="creditability")# woebin_plot(bins)# binning adjustment## adjust breaks interactively# breaks_adj = woebin_adj(dt_f, "creditability", bins)## or specify breaks manuallybreaks_adj=list(age.in.years=c(26,35,40),other.debtors.or.guarantors=c("none","co-applicant%,%guarantor"))bins_adj= woebin(dt_f,y="creditability",breaks_list=breaks_adj)# converting train and test into woe valuesdt_woe_list= lapply(dt_list,function(x) woebin_ply(x,bins_adj))# glm / selecting variables ------m1= glm(creditability~.,family= binomial(),data=dt_woe_list$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$call)# vif(m2, merge_coef = TRUE) # summary(m2)# performance ks & roc ------## predicted proabilitypred_list= lapply(dt_woe_list,function(x) predict(m2,x,type='response'))## Adjusting for oversampling (support.sas.com/kb/22/601.html)# card_prob_adj = scorecard2(bins_adj, dt=dt_list$train, y='creditability',#                x=sub('_woe$','',names(coef(m2))[-1]), badprob_pop=0.03, return_prob=TRUE)## performanceperf= perf_eva(pred=pred_list,label=label_list)# perf_adj = perf_eva(pred = card_prob_adj$prob, label = label_list$train)# score ------## scorecardcard= scorecard(bins_adj,m2)## credit scorescore_list= lapply(dt_list,function(x) scorecard_ply(x,card))## psiperf_psi(score=score_list,label=label_list)# make cutoff decisions -----## gains tablegtbl= gains_table(score= unlist(score_list),label= unlist(label_list))