Optimal Binning for Scoring Modeling

Description

Optimal Binning categorizes a numeric characteristic into bins for ulterior usage in scoring modeling. This process, also known as supervised discretization, utilizes Recursive Partitioning to categorize the numeric characteristic.
The especific algorithm is Conditional Inference Trees which initially excludes missing values (NA) to compute the cutpoints, adding them back later in the process for the calculation of the Information Value.

Usage

smbinning(df, y, x, p = 0.05)

Arguments

Value

The command smbinning generates and object containing the necessary info and utilities for binning. The user should save the output result so it can be used with smbinning.plot, smbinning.sql, and smbinning.gen.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Example: Optimal binning
result=smbinning(df=smbsimdf1,y="fgood",x="cbs1") # Run and save result
result$ivtable # Tabulation and Information Value
result$iv # Information value
result$bands # Bins or bands
result$ctree # Decision tree

Customized Binning

Description

It gives the user the ability to create customized cutpoints.

Usage

smbinning.custom(df, y, x, cuts)

Arguments

Value

The command smbinning.custom generates and object containing the necessary info and utilities for binning. The user should save the output result so it can be used with smbinning.plot, smbinning.sql, and smbinning.gen.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Custom cutpoints using percentiles (20% each)
cbs1cuts=as.vector(quantile(smbsimdf1$cbs1, probs=seq(0,1,0.2), na.rm=TRUE)) # Quantiles
cbs1cuts=cbs1cuts[2:(length(cbs1cuts)-1)] # Remove first (min) and last (max) values

# Example: Customized binning
result=smbinning.custom(df=smbsimdf1,y="fgood",x="cbs1",cuts=cbs1cuts) # Run and save
result$ivtable # Tabulation and Information Value

Exploratory Data Analysis (EDA)

Description

It shows basic statistics for each characteristic in a data frame. The report includes:

• Field: Field name.

• Type: Factor, numeric, integer, other.

• Recs: Number of records.

• Miss: Number of missing records.

• Min: Minimum value.

• Q25: First quartile. It splits off the lowest 25% of data from the highest 75%.

• Q50: Median or second quartile. It cuts data set in half.

• Avg: Average value.

• Q75: Third quartile. It splits off the lowest 75% of data from the highest 25%.

• Max: Maximum value.

• StDv: Standard deviation of a sample.

• Neg: Number of negative values.

• Pos: Number of positive values.

• OutLo: Number of outliers. Records below Q25-1.5*IQR, where IQR=Q75-Q25.

• OutHi: Number of outliers. Records above Q75+1.5*IQR, where IQR=Q75-Q25.

Usage

smbinning.eda(df, rounding = 3, pbar = 1)

Arguments

Value

The command smbinning.eda generates two data frames that list each characteristic with basic statistics such as extreme values and quartiles; and also percentages of missing values and outliers, among others.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Example: Exploratory data analysis of dataset
smbinning.eda(smbsimdf1,rounding=3)$eda # Table with basic statistics
smbinning.eda(smbsimdf1,rounding=3)$edapct # Table with basic percentages

Binning on Factor Variables

Description

It generates a table with relevant metrics for all the categories of a given factor variable.

Usage

smbinning.factor(df, y, x, maxcat = 10)

Arguments

Value

The command smbinning.factor generates and object containing the necessary info and utilities for binning. The user should save the output result so it can be used with smbinning.plot, smbinning.sql, and smbinning.gen.factor.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Binning a factor variable
result=smbinning.factor(smbsimdf1,x="inc",y="fgood", maxcat=11)
result$ivtable

Customized Binning on Factor Variables

Description

It gives the user the ability to combine categories and create new attributes for a given characteristic. Once these new attribues are created in a list (called groups), the funtion generates a table for the uniques values of a given factor variable.

Usage

smbinning.factor.custom(df, y, x, groups)

Arguments

Value

The command smbinning.factor.custom generates an object containing the necessary information and utilities for binning. The user should save the output result so it can be used with smbinning.plot, smbinning.sql, and smbinning.gen.factor.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Example: Customized binning for a factor variable
# Notation: Groups between double quotes
result=smbinning.factor.custom(
  smbsimdf1,x="inc",
  y="fgood",
  c("'W01','W02'",        # Group 1
    "'W03','W04','W05'",  # Group 2
    "'W06','W07'",        # Group 3
    "'W08','W09','W10'")) # Group 4
result$ivtable

Utility to generate a new characteristic from a factor variable

Description

It generates a data frame with a new predictive characteristic from a factor variable after applying smbinning.factor or smbinning.factor.custom.

Usage

smbinning.factor.gen(df, ivout, chrname = "NewChar")

Arguments

Value

A data frame with the binned version of the original characteristic.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data
pop=smbsimdf1 # Set population
train=subset(pop,rnd<=0.7) # Training sample

# Binning a factor variable on training data
result=smbinning.factor(train,x="home",y="fgood")

# Example: Append new binned characteristic to population
pop=smbinning.factor.gen(pop,result,"g1home")

# Split training
train=subset(pop,rnd<=0.7) # Training sample

# Check new field counts
table(train$g1home)
table(pop$g1home)

Utility to generate a new characteristic from a numeric variable

Description

It generates a data frame with a new predictive characteristic after applying smbinning or smbinning.custom.

Usage

smbinning.gen(df, ivout, chrname = "NewChar")

Arguments

Value

A data frame with the binned version of the original characteristic.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data
pop=smbsimdf1 # Set population
train=subset(pop,rnd<=0.7) # Training sample

# Binning application for a numeric variable
result=smbinning(df=train,y="fgood",x="dep") # Run and save result

# Generate a dataset with binned characteristic
pop=smbinning.gen(pop,result,"g1dep")

# Check new field counts
table(pop$g1dep)

Logistic Regression Ranking

Description

It runs all the possible logistic models for a given set of characteristics (chr) and then rank them from highest to lowest performance based on AIC. Important Note: This function may take time depending on the datset size and number of variables used in it. The user should run it at the end of the modeling process once variables have been pre-selected in previous steps.

Usage

smbinning.logitrank(y, chr, df)

Arguments

Value

The command smbinning.logitrank returns a table with the combination of characteristics and their corresponding AIC and deviance. The table is ordered by AIC from lowest (best) to highest.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Example: Best combination of characteristics
smbinning.logitrank(y="fgood",chr=c("chr1","chr2","chr3"),df=smbsimdf3)

Performance Metrics for a Classification Model

Description

It computes the classic performance metrics of a scoring model, including AUC, KS and all the relevant ones from the classification matrix at a specific threshold or cutoff.

Usage

smbinning.metrics(dataset, prediction, actualclass, cutoff = NA,
  report = 1, plot = "none", returndf = 0)

Arguments

Value

The command smbinning.metrics returns a report with classic performance metrics of a classification model.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Example: Metrics Credit Score 1
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  report=1) # Show report
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  cutoff=600, report=1) # User cutoff
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  report=0, plot="auc") # Plot AUC
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  report=0, plot="ks") # Plot KS

# Save table with all details of metrics
cbs1metrics=smbinning.metrics(
  dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
  report=0, returndf=1) # Save metrics details

Visualization of a Classification Matrix

Description

It generates four plots after running and saving the output report from smbinning.metrics.

Usage

smbinning.metrics.plot(df, cutoff = NA, plot = "cmactual")

Arguments

Examples

# Load library and its dataset
library(smbinning)
smbmetricsdf=smbinning.metrics(dataset=smbsimdf1, prediction="cbs1",
                               actualclass="fgood", returndf=1)

# Example 1: Plots based on optimal cutoff
smbinning.metrics.plot(df=smbmetricsdf,plot='cmactual')

# Example 2: Plots using user defined cutoff
smbinning.metrics.plot(df=smbmetricsdf,cutoff=600,plot='cmactual')
smbinning.metrics.plot(df=smbmetricsdf,cutoff=600,plot='cmactualrates')
smbinning.metrics.plot(df=smbmetricsdf,cutoff=600,plot='cmmodel')
smbinning.metrics.plot(df=smbmetricsdf,cutoff=600,plot='cmmodelrates')

Monotonic Binning

Description

It gives the user the ability to impose a monotonic trend for good/bad rates per bin.

Usage

smbinning.monotonic(df, y, x, p = 0.05)

Arguments

Value

The command smbinning.monotonic generates and object containing the necessary info and utilities for binning. The user should save the output result so it can be used with smbinning.plot, smbinning.sql, and smbinning.gen.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Example 1: Monotonic Binning (Increasing Good Rate per Bin)
smbinning(df=smbsimdf2,y="fgood2",x="chr2",p=0.05)$ivtable # Run regular binning
smbinning.monotonic(df=smbsimdf2,y="fgood2",x="chr2",p=0.05)$ivtable # Run monotonic binning
 
# Example 2: Monotonic Binning (Decreasing Good Rate per Bin)
smbinning(df=smbsimdf2,y="fgood3",x="chr3",p=0.05)$ivtable # Run regular binning
smbinning.monotonic(df=smbsimdf2,y="fgood3",x="chr3",p=0.05)$ivtable # Run monotonic binning

Plots after binning

Description

It generates plots for distribution, bad rate, and weight of evidence after running smbinning and saving its output.

Usage

smbinning.plot(ivout, option = "dist", sub = "")

Arguments

Examples

# Load library and its dataset
library(smbinning)

# Example 1: Numeric variable (1 page, 4 plots)
result=smbinning(df=smbsimdf1,y="fgood",x="cbs1") # Run and save result
par(mfrow=c(2,2))
boxplot(smbsimdf1$cbs1~smbsimdf1$fgood,
        horizontal=TRUE, frame=FALSE, col="lightgray",main="Distribution")
mtext("Credit Score",3)
smbinning.plot(result,option="dist",sub="Credit Score")
smbinning.plot(result,option="badrate",sub="Credit Score")
smbinning.plot(result,option="WoE",sub="Credit Score")
par(mfrow=c(1,1))

# Example 2: Factor variable (1 plot per page)
result=smbinning.factor(df=smbsimdf1,y="fgood",x="inc",maxcat=11)
smbinning.plot(result,option="dist",sub="Income Level")
smbinning.plot(result,option="badrate",sub="Income Level")
smbinning.plot(result,option="WoE",sub="Income Level")

Population Stability Index

Description

Often models are developed using multiple periods in time for a number of reasons. For example, to avoid seasonality, to increase the size of the population, and some others. With a metrics like the Population Stability Index (PSI), users can check if there is a significant variation in the distribution of a certain feature by partition (usually time) using the first one as the reference.

Usage

smbinning.psi(df, y, x)

Arguments

Value

Three crosstabs by feature and period that show the frequency (psicnt), percentage (psipct) and PSI (psimg), and a plot for the analyzed characteristic.

Examples

# Load library and its dataset
library(smbinning)

# Check stability for income
smbinning.psi(df=smbsimdf1,y="period",x="inc") 

Scaling

Description

It transforms the coefficients of a logistic regression into scaled points based on the following three parameters pre-selected by the analyst: PDO, Score, and Odds.

Usage

smbinning.scaling(logitraw, pdo = 20, score = 720, odds = 99)

Arguments

Value

A scaled model from a logistic regression built with binned variables, the parameters used in the scaling process, the expected minimum and maximum score, and the original logistic model.

Examples

# Load library and its dataset
library(smbinning)

# Sampling
pop=smbsimdf1 # Population
train=subset(pop,rnd<=0.7) # Training sample

# Generate binning object to generate variables
smbcbs1=smbinning(train,x="cbs1",y="fgood")
smbcbinq=smbinning.factor(train,x="cbinq",y="fgood")
smbcblineut=smbinning.custom(train,x="cblineut",y="fgood",cuts=c(30,40,50))
smbpmt=smbinning.factor(train,x="pmt",y="fgood")
smbtob=smbinning.custom(train,x="tob",y="fgood",cuts=c(1,2,3))
smbdpd=smbinning.factor(train,x="dpd",y="fgood")
smbdep=smbinning.custom(train,x="dep",y="fgood",cuts=c(10000,12000,15000))
smbod=smbinning.factor(train,x="od",y="fgood")
smbhome=smbinning.factor(train,x="home",y="fgood")
smbinc=smbinning.factor.custom(
  train,x="inc",y="fgood",
  c("'W01','W02'","'W03','W04','W05'","'W06','W07'","'W08','W09','W10'"))

pop=smbinning.gen(pop,smbcbs1,"g1cbs1")
pop=smbinning.factor.gen(pop,smbcbinq,"g1cbinq")
pop=smbinning.gen(pop,smbcblineut,"g1cblineut")
pop=smbinning.factor.gen(pop,smbpmt,"g1pmt")
pop=smbinning.gen(pop,smbtob,"g1tob")
pop=smbinning.factor.gen(pop,smbdpd,"g1dpd")
pop=smbinning.gen(pop,smbdep,"g1dep")
pop=smbinning.factor.gen(pop,smbod,"g1od")
pop=smbinning.factor.gen(pop,smbhome,"g1home")
pop=smbinning.factor.gen(pop,smbinc,"g1inc")

# Resample
train=subset(pop,rnd<=0.7) # Training sample
test=subset(pop,rnd>0.7) # Testing sample

# Run logistic regression
f=fgood~g1cbs1+g1cbinq+g1cblineut+g1pmt+g1tob+g1dpd+g1dep+g1od+g1home+g1inc
modlogisticsmb=glm(f,data = train,family = binomial())
summary(modlogisticsmb)

# Example: Scaling from logistic parameters to points
smbscaled=smbinning.scaling(modlogisticsmb,pdo=20,score=720,odds=99)
smbscaled$logitscaled # Scaled model
smbscaled$minmaxscore # Expected minimum and maximum Score
smbscaled$parameters # Parameters used for scaling
summary(smbscaled$logitraw) # Extract of original logistic regression

# Example: Generate score from scaled model
pop1=smbinning.scoring.gen(smbscaled=smbscaled, dataset=pop)

# Example Generate SQL code from scaled model
smbinning.scoring.sql(smbscaled)

Generation of Score and Its Weights

Description

After applying smbinning.scaling to the model, smbinning.scoring generates a data frame with the final Score and additional fields with the points assigned to each characteristic so the user can see how the final score is calculated. Example shown on smbinning.scaling section.

Usage

smbinning.scoring.gen(smbscaled, dataset)

Arguments

Value

The command smbinning.scoring generates a data frame with the final scaled Score and its corresponding scaled weights per characteristic.

Generation of SQL Code After Scaled Model

Description

After applying smbinning.scaling to the model, smbinning.scoring.sql generates a SQL code that creates and updates all variables present in the scaled model. Example shown on smbinning.scaling section.

Usage

smbinning.scoring.sql(smbscaled)

Arguments

Value

The command smbinning.scoring.sql generates a SQL code to implement the model the model in SQL.

SQL Code

Description

It outputs a SQL code to facilitate the generation of new binned characetristic in a SQL environment. User must define table and new characteristic name.

Usage

smbinning.sql(ivout)

Arguments

Value

A text with the SQL code for binning.

Examples

# Load library and its dataset
library(smbinning)

# Example 1: Binning a numeric variable
result=smbinning(df=smbsimdf1,y="fgood",x="cbs1") # Run and save result
smbinning.sql(result)

# Example 2: Binning for a factor variable
result=smbinning.factor(df=smbsimdf1,x="inc",y="fgood",maxcat=11)
smbinning.sql(result)

# Example 3: Customized binning for a factor variable
result=smbinning.factor.custom(
  df=smbsimdf1,x="inc",y="fgood",
  c("'W01','W02'","'W03','W04','W05'",
    "'W06','W07'","'W08','W09','W10'"))
smbinning.sql(result)

Information Value Summary

Description

It gives the user the ability to calculate, in one step, the IV for each characteristic of the dataset. This function also shows a progress bar so the user can see the status of the process.

Usage

smbinning.sumiv(df, y)

Arguments

Value

The command smbinning.sumiv generates a table that lists each characteristic with its corresponding IV for those where the calculation is possible, otherwise it will generate a missing value (NA).

Examples

# Load library and its dataset
library(smbinning)

# Test sample
test=subset(smbsimdf1,rnd>0.9) # Training sample
test$rnd=NULL

# Example: Information Value Summary
testiv=smbinning.sumiv(test,y="fgood")
testiv

# Example: Plot of Information Value Summary
smbinning.sumiv.plot(testiv)

Plot Information Value Summary

Description

It gives the user the ability to plot the Information Value by characteristic. The chart only shows characteristics with a valid IV. Example shown on smbinning.sumiv section.

Usage

smbinning.sumiv.plot(sumivt, cex = 0.9)

Arguments

Value

The command smbinning.sumiv.plot returns a plot that shows the IV for each numeric and factor characteristic in the dataset.

Simulated Credit Data

Description

A simulated dataset where the target variable is fgood, which represents the binary status of default (0) and not default (1).

Format

Data frame with 2,500 rows and 22 columns with 500 defaults.

Details

• fgood: Default (0), Not Default (1).

• cbs1: Credit quality index (1-100).

• cbs2: Profitability index (1-100).

• cbinq: Number of inquiries.

• cbline: Number of credit lines.

• cbterm: Number of term loans.

• cblineut: Line utilization (0-100).

• cbtob: Number of years on file.

• cbdpd: Indicator of days past due on bureau (Yes, No).

• cbnew: Number of new loans.

• pmt: Type of payment (M: Manual, A: Autopay, P: Payroll).

• tob: Time on books (Years).

• dpd: Level of delinquency (No, Low, High).

• dep: Amount of deposits.

• dc: Number of transactions.

• od: Number of overdrafts.

• home: Home ownership indicator (Yes, No).

• inc: Level of income.

• dd: Number of electronic transfers.

• online: Indicator of online activity (Yes, No).

• rnd: Random number to select testing and training samples.

• period: Factor that indicates the year/month of the data (Based on rnd).

Monotonic Binning Sample Data

Description

A simulated dataset used to illustrate the application of monotonic binning.

Format

Data frame with 2,500 rows and 6 columns.

Details

• fgood1: Default (0), Not Default (1) for Numeric Variable 1.

• chr1: Numeric variable 1.

• fgood2: Default (0), Not Default (1) for Numeric Variable 2.

• chr2: Numeric variable 2.

• fgood3: Default (0), Not Default (1) for Numeric Variable 3.

• chr3: Numeric variable 3.

Monotonic Binning Sample Data

Description

A simulated dataset used to illustrate the application of model ranking.

Format

Data frame with 1,000 rows and 4 columns.

Details

• fgood1: Default (0), Not Default (1) for Numeric Variable 1.

• chr1: Numeric variable 1.

• chr2: Numeric variable 2.

• chr3: Numeric variable 3.