README.md

In Zelazny7/binnr: Scorecard modeling in R

What is binnr?

binnr is an R package that helps modelers build scorecards. Scorecard models need to be more than predictive. Regulatory oversight often necessitates they be transparent as well. Model transparency is difficult to enforce in more predictive, non-linear methods such as neural networks, random forest, or gradient boosted decision trees. Often direct modeler intervention is required to ensure proper treatment is given for values of independent variables.

binnr attempts to solve these problems by providing interactive variable manipulation facilities giving the modeler total control over how variables are treated in scorecard models.

Installing binnr

Prerequisites

if(!require(installr)) install.packages("installr")
installr::install.Rtools()

With all of the prerequisites installed, installing binnr is easy:

## used to install packages directly from gitlab
if(!require(devtools)) install.packages("devtools")

devtools::install_git("https://gitlab.ins.risk.regn.net/minneapolis-r-packages/binnr.git", build_vignettes=TRUE)

Binnr Cheat Sheet

From within R, type the following to open a cheat sheet with binnr commands:

vignette("binnr-cheat-sheet")

Overview of binnr

Modeling with binnr follows four phases of development. 1. Bin 2. Fit 3. Review 4. Adjust

The model is usually binned just once, while steps 2 - 4 are repeated as many times as necessary.

Changes from previous versions

The beta version of binnr differs drastically from previous versions in its implementation. Only one scorecard object is created for each modeling endeavor.

All of the operations performed on a Scorecard object are performed "in-place". This is different from how R typically works where the return value from functions has to be assigned to a variable or lost.

Old version

mod2 <- fit(mod1)
mod3 <- adjust(mod2)

beta version

mod$fit("model 1")
mod$adjust()

Bin

library(binnr)
data(titanic)

mod <- bin(data=titanic, y=titanic$Survived)

## optional arguments controlling binning

mod <- bin(
  data = titanic,
  y = titanic$Survived,
  w = weight,
  min.iv = 0.01,
  min.cnt = 100,
  min.res = 25,
  max.bin = 10,
  mono = 2,
  exceptions = c(-1))

## check for multicollinearity

cc <- mod$cluster()
to_drop <- mod$prune_clusters(cc, 0.90, 1)

Fit

mod$fit("model 1", "Initial fit with all variables")

Review

mod$sort()
mod$summary()
mod$compare("model 1", "model 2")
mod$select("model 1")

mod$get_dropped()
mod$get_inmodel()

Adjust

## show and plot are called automatically from the adjust method
mod$variables$Pclass$show()
mod$variables$Pclass$plot()

mod$drop(c("Pclass", "Fare"))
mod$undrop(all=TRUE)

## no longer need to assign to an object
mod$adjust()

## old way of using binnr
# mod2 <- adjust(mod1)

Finalizing the model

Pseudo Pvalues

## run 20 bootstrap samples
pvals <- mod$pseudo_pvals(times=20, bag.fraction=1, replace=TRUE)

## drop variables that have zero coefficients at least 10% of the time
to_drop <- names(which(pvals$pvals > 0.10))

mod$drop(to_drop)

mod$fit("final model")

Generating SAS code

## can calculate RCs from min/max/neutral

code <- mod$gen_code_sas(pfx="my_mod1", method="min")

cat(code, sep="\n", file="path/to/my/model.sas")

Saving/loading a model

saveRDS(mod, "my_model.rds")

mod <- readRDS("my_model.rds")

Zelazny7/binnr documentation built on May 10, 2019, 1:55 a.m.