scorecard
This R package makes the development of credit risk scorecard easily and efficiently by providing functions as follows:
- information value (iv),
- variable filter (var_filter),
- optimal woe binning (woebin, woebin_ply, woebin_plot, woebin_adj),
- scorecard scaling (scorecard, scorecard_ply)
- and performace evaluation (perf_eva, perf_psi).
Installation
- Install the release version of
scorecardfrom CRAN with:
install.packages("scorecard")- Install the latest version of
scorecardfrom github with:
# install.packages("devtools")
devtools::install_github("shichenxie/scorecard")Example
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)
# 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)
)
# Session info ----------------------------------------------------------
# setting value
# version R version 3.4.3 (2017-11-30)
# system x86_64, darwin15.6.0
# ui RStudio (1.1.414)
# language (EN)
# collate en_US.UTF-8
# tz Asia/Shanghai
# date 2018-02-10
#
# Packages --------------------------------------------------------------
# package * version date source
# base * 3.4.3 2017-12-07 local
# codetools 0.2-15 2016-10-05 CRAN (R 3.4.3)
# colorspace 1.3-2 2016-12-14 CRAN (R 3.3.2)
# compiler 3.4.3 2017-12-07 local
# data.table 1.10.4-3 2017-10-27 cran (@1.10.4-)
# datasets * 3.4.3 2017-12-07 local
# devtools 1.13.4 2017-11-09 CRAN (R 3.4.2)
# digest 0.6.14 2018-01-14 CRAN (R 3.4.3)
# doParallel 1.0.11 2017-09-28 CRAN (R 3.4.2)
# foreach 1.4.4 2017-12-12 CRAN (R 3.4.3)
# ggplot2 2.2.1.9000 2018-01-06 Github (hadley/ggplot2@4ee6c94)
# graphics * 3.4.3 2017-12-07 local
# grDevices * 3.4.3 2017-12-07 local
# grid 3.4.3 2017-12-07 local
# gridExtra 2.3 2017-09-09 CRAN (R 3.4.1)
# gtable 0.2.0 2016-02-26 CRAN (R 3.2.3)
# iterators 1.0.9 2017-12-12 CRAN (R 3.4.3)
# lazyeval 0.2.1 2017-10-29 cran (@0.2.1)
# memoise 1.1.0 2017-04-21 CRAN (R 3.3.2)
# methods * 3.4.3 2017-12-07 local
# munsell 0.4.3 2016-02-13 CRAN (R 3.2.3)
# parallel 3.4.3 2017-12-07 local
# plyr 1.8.4 2016-06-08 cran (@1.8.4)
# Rcpp 0.12.15 2018-01-20 CRAN (R 3.4.3)
# rlang 0.1.6 2017-12-21 CRAN (R 3.4.3)
# scales 0.5.0.9000 2017-10-20 Github (hadley/scales@d767915)
# scorecard * 0.1.6 2018-02-10 local
# stats * 3.4.3 2017-12-07 local
# tibble 1.3.4 2017-08-22 CRAN (R 3.4.1)
# tools 3.4.3 2017-12-07 local
# utils * 3.4.3 2017-12-07 local
# withr 2.1.1.9000 2018-01-06 Github (jimhester/withr@df18523)
# yaml 2.1.16 2017-12-12 cran (@2.1.16)