ProductPromotion
Logo

R Programming

made by https://0x3d.site

GitHub - ShichenXie/scorecard: Scorecard Development in R, 评分卡
Scorecard Development in R, 评分卡. Contribute to ShichenXie/scorecard development by creating an account on GitHub.
Visit Site

GitHub - ShichenXie/scorecard: Scorecard Development in R, 评分卡

GitHub - ShichenXie/scorecard: Scorecard Development in R, 评分卡

scorecard

CRAN_Status_Badge

The goal of scorecard package is to make the development of the traditional credit risk scorecard model easier and efficient by providing functions for some common tasks that summarized in below. This package can also used in the development of machine learning models on binary classification.

  • data preprocessing (split_df, replace_na, one_hot, var_scale)
  • weight of evidence (woe) binning (woebin, woebin_plot, woebin_adj, woebin_ply)
  • variable selection (var_filter, iv, vif)
  • performance evaluation (perf_eva, perf_cv, perf_psi)
  • scorecard scaling (scorecard, scorecard2, scorecard_ply)
  • scorecard report (gains_table, report)

Installation

  • Install the release version of scorecard from CRAN with:
install.packages("scorecard")
  • Install the latest version of scorecard from 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 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", 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 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 / 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 proability
pred_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)
                
## performance
perf = perf_eva(pred = pred_list, label = label_list)
# perf_adj = perf_eva(pred = card_prob_adj$prob, label = label_list$train)

# 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)

# make cutoff decisions -----
## gains table
gtbl = gains_table(score = unlist(score_list), label = unlist(label_list))

More Resources
to explore the angular.

mail [email protected] to add your project or resources here 🔥.

Related Articles
to learn about angular.

FAQ's
to learn more about Angular JS.

mail [email protected] to add more queries here 🔍.

More Sites
to check out once you're finished browsing here.

0x3d
https://www.0x3d.site/
0x3d is designed for aggregating information.
NodeJS
https://nodejs.0x3d.site/
NodeJS Online Directory
Cross Platform
https://cross-platform.0x3d.site/
Cross Platform Online Directory
Open Source
https://open-source.0x3d.site/
Open Source Online Directory
Analytics
https://analytics.0x3d.site/
Analytics Online Directory
JavaScript
https://javascript.0x3d.site/
JavaScript Online Directory
GoLang
https://golang.0x3d.site/
GoLang Online Directory
Python
https://python.0x3d.site/
Python Online Directory
Swift
https://swift.0x3d.site/
Swift Online Directory
Rust
https://rust.0x3d.site/
Rust Online Directory
Scala
https://scala.0x3d.site/
Scala Online Directory
Ruby
https://ruby.0x3d.site/
Ruby Online Directory
Clojure
https://clojure.0x3d.site/
Clojure Online Directory
Elixir
https://elixir.0x3d.site/
Elixir Online Directory
Elm
https://elm.0x3d.site/
Elm Online Directory
Lua
https://lua.0x3d.site/
Lua Online Directory
C Programming
https://c-programming.0x3d.site/
C Programming Online Directory
C++ Programming
https://cpp-programming.0x3d.site/
C++ Programming Online Directory
R Programming
https://r-programming.0x3d.site/
R Programming Online Directory
Perl
https://perl.0x3d.site/
Perl Online Directory
Java
https://java.0x3d.site/
Java Online Directory
Kotlin
https://kotlin.0x3d.site/
Kotlin Online Directory
PHP
https://php.0x3d.site/
PHP Online Directory
React JS
https://react.0x3d.site/
React JS Online Directory
Angular
https://angular.0x3d.site/
Angular JS Online Directory