Homework 6
Madalynne Cantu
11/09/2026
Starter code for German credit scoring
Refer to http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data))
for variable description. The response variable is Class
and all others are predictors.
Only run the following code once to install the package
caret. The German credit scoring data in
provided in that package.
install.packages('caret')Task1: Data Preparation
1. Load the caret package and the GermanCredit dataset. (5pts)
library(caret) #this package contains the german data with its numeric format## Loading required package: ggplot2## Loading required package: latticedata(GermanCredit)
GermanCredit$Class <- as.numeric(GermanCredit$Class == "Good") # use this code to convert `Class` into True or False (equivalent to 1 or 0)
GermanCredit$Class <- as.factor(GermanCredit$Class) #make sure `Class` is a factor as SVM require a factor response，now 1 is good and 0 is bad.
str(GermanCredit)## 'data.frame': 1000 obs. of 62 variables:
## $ Duration : int 6 48 12 42 24 36 24 36 12 30 ...
## $ Amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ InstallmentRatePercentage : int 4 2 2 2 3 2 3 2 2 4 ...
## $ ResidenceDuration : int 4 2 3 4 4 4 4 2 4 2 ...
## $ Age : int 67 22 49 45 53 35 53 35 61 28 ...
## $ NumberExistingCredits : int 2 1 1 1 2 1 1 1 1 2 ...
## $ NumberPeopleMaintenance : int 1 1 2 2 2 2 1 1 1 1 ...
## $ Telephone : num 0 1 1 1 1 0 1 0 1 1 ...
## $ ForeignWorker : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Class : Factor w/ 2 levels "0","1": 2 1 2 2 1 2 2 2 2 1 ...
## $ CheckingAccountStatus.lt.0 : num 1 0 0 1 1 0 0 0 0 0 ...
## $ CheckingAccountStatus.0.to.200 : num 0 1 0 0 0 0 0 1 0 1 ...
## $ CheckingAccountStatus.gt.200 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CheckingAccountStatus.none : num 0 0 1 0 0 1 1 0 1 0 ...
## $ CreditHistory.NoCredit.AllPaid : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CreditHistory.ThisBank.AllPaid : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CreditHistory.PaidDuly : num 0 1 0 1 0 1 1 1 1 0 ...
## $ CreditHistory.Delay : num 0 0 0 0 1 0 0 0 0 0 ...
## $ CreditHistory.Critical : num 1 0 1 0 0 0 0 0 0 1 ...
## $ Purpose.NewCar : num 0 0 0 0 1 0 0 0 0 1 ...
## $ Purpose.UsedCar : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Purpose.Furniture.Equipment : num 0 0 0 1 0 0 1 0 0 0 ...
## $ Purpose.Radio.Television : num 1 1 0 0 0 0 0 0 1 0 ...
## $ Purpose.DomesticAppliance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Repairs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Education : num 0 0 1 0 0 1 0 0 0 0 ...
## $ Purpose.Vacation : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Retraining : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Business : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Other : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SavingsAccountBonds.lt.100 : num 0 1 1 1 1 0 0 1 0 1 ...
## $ SavingsAccountBonds.100.to.500 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SavingsAccountBonds.500.to.1000 : num 0 0 0 0 0 0 1 0 0 0 ...
## $ SavingsAccountBonds.gt.1000 : num 0 0 0 0 0 0 0 0 1 0 ...
## $ SavingsAccountBonds.Unknown : num 1 0 0 0 0 1 0 0 0 0 ...
## $ EmploymentDuration.lt.1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ EmploymentDuration.1.to.4 : num 0 1 0 0 1 1 0 1 0 0 ...
## $ EmploymentDuration.4.to.7 : num 0 0 1 1 0 0 0 0 1 0 ...
## $ EmploymentDuration.gt.7 : num 1 0 0 0 0 0 1 0 0 0 ...
## $ EmploymentDuration.Unemployed : num 0 0 0 0 0 0 0 0 0 1 ...
## $ Personal.Male.Divorced.Seperated : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Personal.Female.NotSingle : num 0 1 0 0 0 0 0 0 0 0 ...
## $ Personal.Male.Single : num 1 0 1 1 1 1 1 1 0 0 ...
## $ Personal.Male.Married.Widowed : num 0 0 0 0 0 0 0 0 0 1 ...
## $ Personal.Female.Single : num 0 0 0 0 0 0 0 0 0 0 ...
## $ OtherDebtorsGuarantors.None : num 1 1 1 0 1 1 1 1 1 1 ...
## $ OtherDebtorsGuarantors.CoApplicant : num 0 0 0 0 0 0 0 0 0 0 ...
## $ OtherDebtorsGuarantors.Guarantor : num 0 0 0 1 0 0 0 0 0 0 ...
## $ Property.RealEstate : num 1 1 1 0 0 0 0 0 1 0 ...
## $ Property.Insurance : num 0 0 0 1 0 0 1 0 0 0 ...
## $ Property.CarOther : num 0 0 0 0 0 0 0 1 0 1 ...
## $ Property.Unknown : num 0 0 0 0 1 1 0 0 0 0 ...
## $ OtherInstallmentPlans.Bank : num 0 0 0 0 0 0 0 0 0 0 ...
## $ OtherInstallmentPlans.Stores : num 0 0 0 0 0 0 0 0 0 0 ...
## $ OtherInstallmentPlans.None : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Housing.Rent : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Housing.Own : num 1 1 1 0 0 0 1 0 1 1 ...
## $ Housing.ForFree : num 0 0 0 1 1 1 0 0 0 0 ...
## $ Job.UnemployedUnskilled : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Job.UnskilledResident : num 0 0 1 0 0 1 0 0 1 0 ...
## $ Job.SkilledEmployee : num 1 1 0 1 1 0 1 0 0 0 ...
## $ Job.Management.SelfEmp.HighlyQualified: num 0 0 0 0 0 0 0 1 0 1 ...# This is the code that drop variables that provide no information in the data
# Just run it
GermanCredit = GermanCredit[,-c(14,19,27,30,35,40,44,45,48,52,55,58,62)]2. Explore the dataset to understand its structure. It’s okay to use same code from last homework. (2.5pts)
head(GermanCredit)## Duration Amount InstallmentRatePercentage ResidenceDuration Age
## 1 6 1169 4 4 67
## 2 48 5951 2 2 22
## 3 12 2096 2 3 49
## 4 42 7882 2 4 45
## 5 24 4870 3 4 53
## 6 36 9055 2 4 35
## NumberExistingCredits NumberPeopleMaintenance Telephone ForeignWorker Class
## 1 2 1 0 1 1
## 2 1 1 1 1 0
## 3 1 2 1 1 1
## 4 1 2 1 1 1
## 5 2 2 1 1 0
## 6 1 2 0 1 1
## CheckingAccountStatus.lt.0 CheckingAccountStatus.0.to.200
## 1 1 0
## 2 0 1
## 3 0 0
## 4 1 0
## 5 1 0
## 6 0 0
## CheckingAccountStatus.gt.200 CreditHistory.NoCredit.AllPaid
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## CreditHistory.ThisBank.AllPaid CreditHistory.PaidDuly CreditHistory.Delay
## 1 0 0 0
## 2 0 1 0
## 3 0 0 0
## 4 0 1 0
## 5 0 0 1
## 6 0 1 0
## Purpose.NewCar Purpose.UsedCar Purpose.Furniture.Equipment
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 1
## 5 1 0 0
## 6 0 0 0
## Purpose.Radio.Television Purpose.DomesticAppliance Purpose.Repairs
## 1 1 0 0
## 2 1 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Purpose.Education Purpose.Retraining Purpose.Business
## 1 0 0 0
## 2 0 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 0
## 6 1 0 0
## SavingsAccountBonds.lt.100 SavingsAccountBonds.100.to.500
## 1 0 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 6 0 0
## SavingsAccountBonds.500.to.1000 SavingsAccountBonds.gt.1000
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## EmploymentDuration.lt.1 EmploymentDuration.1.to.4 EmploymentDuration.4.to.7
## 1 0 0 0
## 2 0 1 0
## 3 0 0 1
## 4 0 0 1
## 5 0 1 0
## 6 0 1 0
## EmploymentDuration.gt.7 Personal.Male.Divorced.Seperated
## 1 1 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Personal.Female.NotSingle Personal.Male.Single OtherDebtorsGuarantors.None
## 1 0 1 1
## 2 1 0 1
## 3 0 1 1
## 4 0 1 0
## 5 0 1 1
## 6 0 1 1
## OtherDebtorsGuarantors.CoApplicant Property.RealEstate Property.Insurance
## 1 0 1 0
## 2 0 1 0
## 3 0 1 0
## 4 0 0 1
## 5 0 0 0
## 6 0 0 0
## Property.CarOther OtherInstallmentPlans.Bank OtherInstallmentPlans.Stores
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Housing.Rent Housing.Own Job.UnemployedUnskilled Job.UnskilledResident
## 1 0 1 0 0
## 2 0 1 0 0
## 3 0 1 0 1
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 1
## Job.SkilledEmployee
## 1 1
## 2 1
## 3 0
## 4 1
## 5 1
## 6 0dim(GermanCredit)## [1] 1000 49str(GermanCredit)## 'data.frame': 1000 obs. of 49 variables:
## $ Duration : int 6 48 12 42 24 36 24 36 12 30 ...
## $ Amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ InstallmentRatePercentage : int 4 2 2 2 3 2 3 2 2 4 ...
## $ ResidenceDuration : int 4 2 3 4 4 4 4 2 4 2 ...
## $ Age : int 67 22 49 45 53 35 53 35 61 28 ...
## $ NumberExistingCredits : int 2 1 1 1 2 1 1 1 1 2 ...
## $ NumberPeopleMaintenance : int 1 1 2 2 2 2 1 1 1 1 ...
## $ Telephone : num 0 1 1 1 1 0 1 0 1 1 ...
## $ ForeignWorker : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Class : Factor w/ 2 levels "0","1": 2 1 2 2 1 2 2 2 2 1 ...
## $ CheckingAccountStatus.lt.0 : num 1 0 0 1 1 0 0 0 0 0 ...
## $ CheckingAccountStatus.0.to.200 : num 0 1 0 0 0 0 0 1 0 1 ...
## $ CheckingAccountStatus.gt.200 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CreditHistory.NoCredit.AllPaid : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CreditHistory.ThisBank.AllPaid : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CreditHistory.PaidDuly : num 0 1 0 1 0 1 1 1 1 0 ...
## $ CreditHistory.Delay : num 0 0 0 0 1 0 0 0 0 0 ...
## $ Purpose.NewCar : num 0 0 0 0 1 0 0 0 0 1 ...
## $ Purpose.UsedCar : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Purpose.Furniture.Equipment : num 0 0 0 1 0 0 1 0 0 0 ...
## $ Purpose.Radio.Television : num 1 1 0 0 0 0 0 0 1 0 ...
## $ Purpose.DomesticAppliance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Repairs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Education : num 0 0 1 0 0 1 0 0 0 0 ...
## $ Purpose.Retraining : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purpose.Business : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SavingsAccountBonds.lt.100 : num 0 1 1 1 1 0 0 1 0 1 ...
## $ SavingsAccountBonds.100.to.500 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SavingsAccountBonds.500.to.1000 : num 0 0 0 0 0 0 1 0 0 0 ...
## $ SavingsAccountBonds.gt.1000 : num 0 0 0 0 0 0 0 0 1 0 ...
## $ EmploymentDuration.lt.1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ EmploymentDuration.1.to.4 : num 0 1 0 0 1 1 0 1 0 0 ...
## $ EmploymentDuration.4.to.7 : num 0 0 1 1 0 0 0 0 1 0 ...
## $ EmploymentDuration.gt.7 : num 1 0 0 0 0 0 1 0 0 0 ...
## $ Personal.Male.Divorced.Seperated : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Personal.Female.NotSingle : num 0 1 0 0 0 0 0 0 0 0 ...
## $ Personal.Male.Single : num 1 0 1 1 1 1 1 1 0 0 ...
## $ OtherDebtorsGuarantors.None : num 1 1 1 0 1 1 1 1 1 1 ...
## $ OtherDebtorsGuarantors.CoApplicant: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Property.RealEstate : num 1 1 1 0 0 0 0 0 1 0 ...
## $ Property.Insurance : num 0 0 0 1 0 0 1 0 0 0 ...
## $ Property.CarOther : num 0 0 0 0 0 0 0 1 0 1 ...
## $ OtherInstallmentPlans.Bank : num 0 0 0 0 0 0 0 0 0 0 ...
## $ OtherInstallmentPlans.Stores : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Housing.Rent : num 0 0 0 0 0 0 0 1 0 0 ...
## $ Housing.Own : num 1 1 1 0 0 0 1 0 1 1 ...
## $ Job.UnemployedUnskilled : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Job.UnskilledResident : num 0 0 1 0 0 1 0 0 1 0 ...
## $ Job.SkilledEmployee : num 1 1 0 1 1 0 1 0 0 0 ...summary(GermanCredit)## Duration Amount InstallmentRatePercentage ResidenceDuration
## Min. : 4.0 Min. : 250 Min. :1.000 Min. :1.000
## 1st Qu.:12.0 1st Qu.: 1366 1st Qu.:2.000 1st Qu.:2.000
## Median :18.0 Median : 2320 Median :3.000 Median :3.000
## Mean :20.9 Mean : 3271 Mean :2.973 Mean :2.845
## 3rd Qu.:24.0 3rd Qu.: 3972 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :72.0 Max. :18424 Max. :4.000 Max. :4.000
## Age NumberExistingCredits NumberPeopleMaintenance Telephone
## Min. :19.00 Min. :1.000 Min. :1.000 Min. :0.000
## 1st Qu.:27.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.000
## Median :33.00 Median :1.000 Median :1.000 Median :1.000
## Mean :35.55 Mean :1.407 Mean :1.155 Mean :0.596
## 3rd Qu.:42.00 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :75.00 Max. :4.000 Max. :2.000 Max. :1.000
## ForeignWorker Class CheckingAccountStatus.lt.0
## Min. :0.000 0:300 Min. :0.000
## 1st Qu.:1.000 1:700 1st Qu.:0.000
## Median :1.000 Median :0.000
## Mean :0.963 Mean :0.274
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## CheckingAccountStatus.0.to.200 CheckingAccountStatus.gt.200
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.269 Mean :0.063
## 3rd Qu.:1.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## CreditHistory.NoCredit.AllPaid CreditHistory.ThisBank.AllPaid
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.000
## Mean :0.04 Mean :0.049
## 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.00 Max. :1.000
## CreditHistory.PaidDuly CreditHistory.Delay Purpose.NewCar Purpose.UsedCar
## Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.00 Median :0.000 Median :0.000 Median :0.000
## Mean :0.53 Mean :0.088 Mean :0.234 Mean :0.103
## 3rd Qu.:1.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.00 Max. :1.000 Max. :1.000 Max. :1.000
## Purpose.Furniture.Equipment Purpose.Radio.Television Purpose.DomesticAppliance
## Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000
## Mean :0.181 Mean :0.28 Mean :0.012
## 3rd Qu.:0.000 3rd Qu.:1.00 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000
## Purpose.Repairs Purpose.Education Purpose.Retraining Purpose.Business
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000 Median :0.000
## Mean :0.022 Mean :0.05 Mean :0.009 Mean :0.097
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000
## SavingsAccountBonds.lt.100 SavingsAccountBonds.100.to.500
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :0.000
## Mean :0.603 Mean :0.103
## 3rd Qu.:1.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## SavingsAccountBonds.500.to.1000 SavingsAccountBonds.gt.1000
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.063 Mean :0.048
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## EmploymentDuration.lt.1 EmploymentDuration.1.to.4 EmploymentDuration.4.to.7
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.172 Mean :0.339 Mean :0.174
## 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## EmploymentDuration.gt.7 Personal.Male.Divorced.Seperated
## Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.00
## Mean :0.253 Mean :0.05
## 3rd Qu.:1.000 3rd Qu.:0.00
## Max. :1.000 Max. :1.00
## Personal.Female.NotSingle Personal.Male.Single OtherDebtorsGuarantors.None
## Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:1.000
## Median :0.00 Median :1.000 Median :1.000
## Mean :0.31 Mean :0.548 Mean :0.907
## 3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.00 Max. :1.000 Max. :1.000
## OtherDebtorsGuarantors.CoApplicant Property.RealEstate Property.Insurance
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.041 Mean :0.282 Mean :0.232
## 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## Property.CarOther OtherInstallmentPlans.Bank OtherInstallmentPlans.Stores
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.332 Mean :0.139 Mean :0.047
## 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## Housing.Rent Housing.Own Job.UnemployedUnskilled Job.UnskilledResident
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.0
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.0
## Median :0.000 Median :1.000 Median :0.000 Median :0.0
## Mean :0.179 Mean :0.713 Mean :0.022 Mean :0.2
## 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.0
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.0
## Job.SkilledEmployee
## Min. :0.00
## 1st Qu.:0.00
## Median :1.00
## Mean :0.63
## 3rd Qu.:1.00
## Max. :1.00table(GermanCredit$Class)##
## 0 1
## 300 700sum(is.na(GermanCredit))## [1] 0Your observation: The GermanCredit dataset contains both numerical and categorical variables that describe customers’ credit information. The Class variable identifies whether a customer has good or bad credit. Exploring the dataset using summary statistics and structure functions helps us understand the variables, their data types, and the distribution of credit classifications before building the SVM model.
3. Split the dataset into training and test set
with 80-20 split. Please use the random seed as 2024 for
reproducibility. (2.5pts)
# Set the seed
set.seed(2024)
trainIndex <- createDataPartition(
GermanCredit$Class,
p = 0.8,
list = FALSE
)
training <- GermanCredit[trainIndex, ]
testing <- GermanCredit[-trainIndex, ]
# Check dataset dimensions
dim(training)## [1] 800 49dim(testing)## [1] 200 49Your observation: The dataset was divided into 80% training data and 20% testing data. Using set.seed(2024) ensures that the same random split can be reproduced each time the code is run. The createDataPartition() function also maintains a similar proportion of good and bad credit observations in both datasets.
Task 2: SVM without weighted class cost (15pts)
1. Fit a SVM model using the training set with linear kernel. Please use all variables, but make sure the variable types are right. If running on old laptop, could take some time! (5pts)
library(e1071)##
## Attaching package: 'e1071'## The following object is masked from 'package:ggplot2':
##
## element# Fit the SVM model using the training data
svm_model <- svm(
Class ~ .,
data = training,
kernel = "linear"
)
# Display the model results
print(svm_model)##
## Call:
## svm(formula = Class ~ ., data = training, kernel = "linear")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 1
##
## Number of Support Vectors: 403Your observation: The SVM model was trained using the training dataset with a linear kernel. All available predictor variables were included to predict whether a customer has good or bad credit. The response variable was kept as a factor so the model could perform classification.
2. Use the training set to get predicted classes. (2.5pts)
# Predict classes using the training data
svm_train_pred <- predict(svm_model, newdata = training)
# View the first six predictions
head(svm_train_pred)## 1 2 3 4 5 6
## 1 0 1 1 0 1
## Levels: 0 1Your observation: The trained SVM model was used to predict credit classifications for the training dataset. The predictions identify whether each customer is classified as having good or bad credit.
3. Obtain confusion matrix and MR on training set. (2.5pts)
# Obtain the confusion matrix
confusion_matrix <- table(
Actual = training$Class,
Predicted = svm_train_pred
)
print(confusion_matrix)## Predicted
## Actual 0 1
## 0 148 92
## 1 68 492# Calculate misclassification rate
MR_train <- mean(svm_train_pred != training$Class)
print(paste("Training Misclassification Rate:", MR_train))## [1] "Training Misclassification Rate: 0.2"Your observation: The confusion matrix compares the actual credit classifications with the classifications predicted by the SVM model. The misclassification rate measures the percentage of incorrectly classified observations. A lower misclassification rate indicates better prediction accuracy on the training dataset.
4. Use the testing set to get predicted classes. (2.5pts)
svm_test_pred <- predict(svm_model, newdata = testing)
head(svm_test_pred)## 17 21 24 28 33 46
## 1 1 1 1 1 1
## Levels: 0 1Your observation: The trained SVM model was used to predict credit classifications for the testing dataset. These predictions show whether each customer is classified as having good or bad credit.
5. Obtain confusion matrix and MR on testing set. (2.5pts)
confusion_matrix_test <- table(
Actual = testing$Class,
Predicted = svm_test_pred
)
print(confusion_matrix_test)## Predicted
## Actual 0 1
## 0 31 29
## 1 24 116MR_test <- mean(svm_test_pred != testing$Class)
print(paste("Testing Misclassification Rate:", MR_test))## [1] "Testing Misclassification Rate: 0.265"Your observation: The confusion matrix compares the actual credit classifications in the testing dataset with the classifications predicted by the SVM model. The testing misclassification rate measures the proportion of predictions that were incorrect. This helps evaluate how well the model performs on data it was not trained on.
Task 3: SVM with weighted class cost, and probabilities enabled (17.5pts ,each 2.5pts)
1. Fit a SVM model using the training set with weight of 2
on “1” and weight of 1 on “0”. Please use all variables, but make sure
the variable types are right. Also, enable probability fitting with
probability = TRUE.
svm_weighted <- svm(
Class ~ .,
data = training,
kernel = "linear",
class.weights = c("0" = 1, "1" = 2),
probability = TRUE
)
print(svm_weighted)##
## Call:
## svm(formula = Class ~ ., data = training, kernel = "linear", class.weights = c(`0` = 1,
## `1` = 2), probability = TRUE)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 1
##
## Number of Support Vectors: 378Your observation: The weighted SVM model was trained using the training dataset with a linear kernel. A weight of 2 was assigned to class 1 and a weight of 1 to class 0 to give greater importance to correctly classifying class 1. Probability fitting was also enabled to allow the model to estimate classification probabilities.
2. Use the training set to get predicted probabilities and classes.
# Predict classes
svm_train_pred_weighted <- predict(
svm_weighted,
newdata = training,
probability = TRUE
)
# Extract predicted probabilities
svm_train_prob <- attr(svm_train_pred_weighted, "probabilities")
# View predicted classes
head(svm_train_pred_weighted)## 1 2 3 4 5 6
## 1 0 1 1 0 1
## Levels: 0 1# View predicted probabilities
head(svm_train_prob)## 1 0
## 1 0.8407723 0.15922768
## 2 0.4059899 0.59401014
## 3 0.9206866 0.07931335
## 4 0.7775040 0.22249604
## 5 0.2748831 0.72511691
## 6 0.7380708 0.26192923Your observation: The weighted SVM model was used to predict credit classifications and their corresponding probabilities for the training dataset. The predicted classes show whether each customer is classified as having good or bad credit, while the probabilities indicate the model’s confidence in each classification.
3. Obtain confusion matrix and MR on training set (use predicted classes).
confusion_matrix_weighted_train <- table(
Actual = training$Class,
Predicted = svm_train_pred_weighted
)
print(confusion_matrix_weighted_train)## Predicted
## Actual 0 1
## 0 101 139
## 1 24 536MR_weighted_train <- mean(
svm_train_pred_weighted != training$Class
)
print(paste("Training Misclassification Rate:", MR_weighted_train))## [1] "Training Misclassification Rate: 0.20375"Your observation: The confusion matrix compares the actual credit classifications with the classes predicted by the weighted SVM model. The misclassification rate measures the proportion of incorrect predictions on the training set.
4. Obtain ROC and AUC on training set (use predicted probabilities).
library(pROC)## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var# Obtain probability of class 1
train_roc <- roc(
response = training$Class,
predictor = svm_train_prob[, "1"]
)## Setting levels: control = 0, case = 1## Setting direction: controls < cases# Plot ROC curve
plot(train_roc, main = "ROC Curve - Weighted SVM Training Set")
# Calculate AUC
auc(train_roc)## Area under the curve: 0.825Your observation: The ROC curve evaluates how well the weighted SVM model distinguishes between the two credit classifications. The AUC measures the model’s ability to separate the classes, with a higher AUC indicating better classification performance.
5. Use the testing set to get predicted probabilities and classes.
# Predict classes
svm_test_pred_weighted <- predict(
svm_weighted,
newdata = testing,
probability = TRUE
)
# Extract predicted probabilities
svm_test_prob <- attr(svm_test_pred_weighted, "probabilities")
# View predicted classes
head(svm_test_pred_weighted)## 17 21 24 28 33 46
## 1 1 1 1 1 1
## Levels: 0 1# View predicted probabilities
head(svm_test_prob)## 1 0
## 17 0.8571835 0.1428165
## 21 0.8560695 0.1439305
## 24 0.8688477 0.1311523
## 28 0.8136918 0.1863082
## 33 0.7015288 0.2984712
## 46 0.7625487 0.2374513Your observation: The weighted SVM model was used to predict credit classifications and their corresponding probabilities for the testing dataset. The predicted classes show whether each customer is classified as having good or bad credit, while the probabilities indicate the model’s confidence in each prediction.
6. Obtain confusion matrix and MR on testing set. (use predicted classes).
svm_test_pred_weighted <- predict(
svm_weighted,
newdata = testing,
probability = TRUE
)
svm_test_prob <- attr(
svm_test_pred_weighted,
"probabilities"
)
head(svm_test_pred_weighted)## 17 21 24 28 33 46
## 1 1 1 1 1 1
## Levels: 0 1head(svm_test_prob)## 1 0
## 17 0.8571835 0.1428165
## 21 0.8560695 0.1439305
## 24 0.8688477 0.1311523
## 28 0.8136918 0.1863082
## 33 0.7015288 0.2984712
## 46 0.7625487 0.2374513confusion_matrix_weighted_test <- table(
Actual = testing$Class,
Predicted = svm_test_pred_weighted
)
print(confusion_matrix_weighted_test)## Predicted
## Actual 0 1
## 0 21 39
## 1 14 126MR_weighted_test <- mean(
svm_test_pred_weighted != testing$Class
)
print(paste("Testing Misclassification Rate:", MR_weighted_test))## [1] "Testing Misclassification Rate: 0.265"Your observation:
7. Obtain ROC and AUC on testing set. (use predicted probabilities).
test_roc <- roc(
response = testing$Class,
predictor = svm_test_prob[, "1"]
)## Setting levels: control = 0, case = 1## Setting direction: controls < cases# Plot ROC curve
plot(test_roc, main = "ROC Curve - Weighted SVM Testing Set")
# Calculate AUC
auc(test_roc)## Area under the curve: 0.7115Your observation:
Task 4: Conclusion (7.5pts)
1. Summarize your findings and discuss what you observed from the above analysis. (2.5pts)
The SVM analysis showed how well the model could predict whether customers had good or bad credit. Using weighted class costs gave more importance to correctly identifying class 1 by assigning it twice the weight of class 0. The confusion matrices and misclassification rates helped evaluate the model’s accuracy on both the training and testing datasets. The ROC curves and AUC values provided another way to measure how well the model separated the two credit classes. Overall, comparing the training and testing results helps determine how well the model performs on new data rather than just the data it was trained on.
2. Please recall the results from last homework, how do you compare SVM to logistic regression? No coding is required for this question, just discuss. (5pts)
Both SVM and logistic regression were used to predict whether customers had good or bad credit using the GermanCredit dataset. Logistic regression estimates the probability of belonging to a class and makes it easier to interpret how individual variables affect the prediction. SVM focuses on finding the best boundary between the two classes. The weighted SVM model also gives more importance to class 1, which can help address differences in class importance. Overall, both methods can be useful for classification, but their performance should be compared using testing misclassification rates and AUC values to determine which model predicts credit classifications more accurately.
The previous logistic regression homework used the same GermanCredit dataset and a 700-observation training set with a 300-observation testing set. However, I don’t have the actual logistic regression testing MR or AUC results available, so I don’t want to claim that one model performed better without comparing the numbers.