Something is wrong; all the ROC metric values are missing:

Question

I'm training a model in R with the caret package:

ctrl <- trainControl(method = "repeatedcv", repeats = 3,  summaryFunction = twoClassSummary)

logitBoostFit <- train(LoanStatus~., credit, method = "LogitBoost", family=binomial, preProcess=c("center", "scale", "pca"), 
    trControl = ctrl)

I'm getting the following warnings:

Warning message:
In train.default(x, y, weights = w, ...): The metric "Accuracy" was not in the result set. ROC will be used instead.Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
Something is wrong; all the ROC metric values are missing:
      ROC           Sens              Spec       
 Min.   : NA   Min.   :0.03496   Min.   :0.9747  
 1st Qu.: NA   1st Qu.:0.03919   1st Qu.:0.9758  
 Median : NA   Median :0.04343   Median :0.9770  
 Mean   :NaN   Mean   :0.04349   Mean   :0.9779  
 3rd Qu.: NA   3rd Qu.:0.04776   3rd Qu.:0.9795  
 Max.   : NA   Max.   :0.05210   Max.   :0.9821  
 NA's   :3                                       
Error in train.default(x, y, weights = w, ...): Stopping

I installed the pROC package:

install.packages("pROC", repos="http://cran.rstudio.com/")
library(pROC)
Type 'citation("pROC")' for a citation.

Attaching package: ‘pROC’

The following objects are masked from ‘package:stats’:

    cov, smooth, var

Here's the data:

str(credit)
'data.frame':   8580 obs. of  45 variables:
 $ ListingCategory            : int  1 7 3 1 1 7 1 1 1 1 ...
 $ IncomeRange                : int  3 4 6 4 4 3 3 4 3 3 ...
 $ StatedMonthlyIncome        : num  2583 4326 10500 4167 5667 ...
 $ IncomeVerifiable           : logi  TRUE TRUE TRUE FALSE TRUE TRUE ...
 $ DTIwProsperLoan            : num  1.8e-01 2.0e-01 1.7e-01 1.0e+06 1.8e-01 4.4e-01 2.2e-01 2.0e-01 2.0e-01 3.1e-01 ...
 $ EmploymentStatusDescription: Factor w/ 7 levels "Employed","Full-time",..: 1 4 1 7 1 1 1 1 1 1 ...
 $ Occupation                 : Factor w/ 65 levels "","Accountant/CPA",..: 37 37 20 14 43 58 48 37 37 37 ...
 $ MonthsEmployed             : int  4 44 159 67 26 16 209 147 24 9 ...
 $ BorrowerState              : Factor w/ 48 levels "AK","AL","AR",..: 22 32 5 5 14 28 4 10 10 34 ...
 $ BorrowerCity               : Factor w/ 3089 levels "AARONSBURG","ABERDEEN",..: 1737 3059 2488 654 482 719 895 1699 2747 1903 ...
 $ BorrowerMetropolitanArea   : Factor w/ 1 level "(Not Implemented)": 1 1 1 1 1 1 1 1 1 1 ...
 $ LenderIndicator            : int  0 0 0 1 0 0 0 0 1 0 ...
 $ GroupIndicator             : logi  FALSE FALSE FALSE TRUE FALSE FALSE ...
 $ GroupName                  : Factor w/ 83 levels "","00 Used Car Loans",..: 1 1 1 47 1 1 1 1 1 1 ...
 $ ChannelCode                : int  90000 90000 90000 80000 40000 40000 90000 90000 80000 90000 ...
 $ AmountParticipation        : int  0 0 0 0 0 0 0 0 0 0 ...
 $ MonthlyDebt                : int  247 785 1631 817 644 1524 427 817 654 749 ...
 $ CurrentDelinquencies       : int  0 0 0 0 0 0 0 1 0 1 ...
 $ DelinquenciesLast7Years    : int  0 10 0 0 0 0 0 0 0 0 ...
 $ PublicRecordsLast10Years   : int  0 1 0 0 0 0 1 0 1 0 ...
 $ PublicRecordsLast12Months  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ FirstRecordedCreditLine    : Factor w/ 4719 levels "1/1/00 0:00",..: 3032 2673 1197 2541 4698 4345 3150 925 4452 2358 ...
 $ CreditLinesLast7Years      : int  53 30 36 26 7 22 15 20 34 32 ...
 $ InquiriesLast6Months       : int  2 8 5 0 0 0 0 3 0 0 ...
 $ AmountDelinquent           : int  0 0 0 0 0 0 0 63 0 15 ...
 $ CurrentCreditLines         : int  10 10 18 10 4 11 6 10 7 8 ...
 $ OpenCreditLines            : int  9 10 15 8 3 8 5 7 7 8 ...
 $ BankcardUtilization        : num  0.26 0.69 0.94 0.69 0.81 0.38 0.55 0.24 0.03 0 ...
 $ TotalOpenRevolvingAccounts : int  9 7 12 10 3 5 4 5 4 6 ...
 $ InstallmentBalance         : int  48648 14827 0 0 0 30916 0 21619 41340 15447 ...
 $ RealEstateBalance          : int  0 0 577745 0 0 0 191296 0 0 126039 ...
 $ RevolvingBalance           : int  5265 9967 94966 50511 37871 22463 19550 2436 1223 3236 ...
 $ RealEstatePayment          : int  0 0 4159 0 0 0 1303 0 0 1279 ...
 $ RevolvingAvailablePercent  : int  78 52 36 45 18 61 44 74 96 76 ...
 $ TotalInquiries             : int  8 11 15 2 0 0 1 7 1 1 ...
 $ TotalTradeItems            : int  53 30 36 26 7 22 15 20 34 32 ...
 $ SatisfactoryAccounts       : int  52 23 36 26 7 19 15 18 34 29 ...
 $ NowDelinquentDerog         : int  0 0 0 0 0 0 0 1 0 1 ...
 $ WasDelinquentDerog         : int  1 7 0 0 0 3 0 1 0 2 ...
 $ OldestTradeOpenDate        : int  5092001 5011977 12011984 4272000 9081993 9122000 6161987 11181999 9191990 4132000 ...
 $ DelinquenciesOver30Days    : int  0 6 0 0 0 13 0 2 0 2 ...
 $ DelinquenciesOver60Days    : int  0 4 0 0 0 0 0 0 0 1 ...
 $ DelinquenciesOver90Days    : int  0 10 0 0 0 0 0 0 0 0 ...
 $ IsHomeowner                : logi  FALSE FALSE TRUE FALSE FALSE FALSE ...
 $ LoanStatus                 : Factor w/ 2 levels "0","1": 2 1 1 2 2 2 2 2 2 1 .`..

summary(credit) ListingCategory IncomeRange StatedMonthlyIncome IncomeVerifiable Min. : 0.000 Min. :1.000 Min. : 0 Mode :logical
1st Qu.: 1.000 1st Qu.:3.000 1st Qu.: 3167 FALSE:784
Median : 2.000 Median :4.000 Median : 4750 TRUE :7796
Mean : 4.997 Mean :4.089 Mean : 5755 NA's :0
3rd Qu.: 7.000 3rd Qu.:5.000 3rd Qu.: 7083
Max. :20.000 Max. :7.000 Max. :250000

DTIwProsperLoan EmploymentStatusDescription MonthsEmployed
Min. : 0.0 Employed :7182 Min. :-23.00
1st Qu.: 0.1 Full-time : 416 1st Qu.: 26.00
Median : 0.2 Not employed : 122 Median : 68.00
Mean : 91609.4 Other : 475 Mean : 97.44
3rd Qu.: 0.3 Part-time : 7 3rd Qu.:139.00
Max. :1000000.0 Retired : 32 Max. :755.00
Self-employed: 346 NA's :5
BorrowerState LenderIndicator GroupIndicator ChannelCode
CA :1056 Min. :0.00000 Mode :logical Min. :40000
FL : 608 1st Qu.:0.00000 FALSE:8325 1st Qu.:80000
NY : 574 Median :0.00000 TRUE :255 Median :80000
TX : 532 Mean :0.09196 NA's :0 Mean :77196
IL : 443 3rd Qu.:0.00000 3rd Qu.:90000
GA : 343 Max. :1.00000 Max. :90000
(Other):5024
MonthlyDebt CurrentDelinquencies DelinquenciesLast7Years Min. : 0.0 Min. : 0.0000 Min. : 0.000
1st Qu.: 364.0 1st Qu.: 0.0000 1st Qu.: 0.000
Median : 708.0 Median : 0.0000 Median : 0.000
Mean : 885.5 Mean : 0.4119 Mean : 4.009
3rd Qu.: 1205.2 3rd Qu.: 0.0000 3rd Qu.: 3.000
Max. :30213.0 Max. :21.0000 Max. :99.000

PublicRecordsLast10Years PublicRecordsLast12Months CreditLinesLast7Years Min. : 0.0000 Min. :0.00000 Min. : 2.0
1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 16.0
Median : 0.0000 Median :0.00000 Median : 24.0
Mean : 0.2809 Mean :0.01364 Mean : 26.1
3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 34.0
Max. :11.0000 Max. :4.00000 Max. :115.0

InquiriesLast6Months AmountDelinquent CurrentCreditLines OpenCreditLines Min. : 0.0000 Min. : 0 Min. : 0.000 Min. : 0.000
1st Qu.: 0.0000 1st Qu.: 0 1st Qu.: 5.000 1st Qu.: 5.000
Median : 1.0000 Median : 0 Median : 9.000 Median : 8.000
Mean : 0.9994 Mean : 1195 Mean : 9.345 Mean : 8.306
3rd Qu.: 1.0000 3rd Qu.: 0 3rd Qu.:12.000 3rd Qu.:11.000
Max. :15.0000 Max. :179158 Max. :54.000 Max. :42.000

BankcardUtilization TotalOpenRevolvingAccounts InstallmentBalance Min. :0.0000 Min. : 0.000 Min. : 0
1st Qu.:0.2500 1st Qu.: 3.000 1st Qu.: 3338
Median :0.5400 Median : 6.000 Median : 14453
Mean :0.5182 Mean : 6.441 Mean : 24900
3rd Qu.:0.7900 3rd Qu.: 9.000 3rd Qu.: 32238
Max. :2.2300 Max. :44.000 Max. :739371
NA's :328
RealEstateBalance RevolvingBalance RealEstatePayment RevolvingAvailablePercent Min. : 0 Min. : 0 Min. : 0.0 Min. : 0.00
1st Qu.: 0 1st Qu.: 2799 1st Qu.: 0.0 1st Qu.: 29.00
Median : 26154 Median : 8784 Median : 346.5 Median : 52.00
Mean : 109306 Mean : 19555 Mean : 830.5 Mean : 51.46
3rd Qu.: 176542 3rd Qu.: 21110 3rd Qu.: 1382.2 3rd Qu.: 75.00
Max. :1938421 Max. :695648 Max. :13651.0 Max. :100.00

TotalInquiries TotalTradeItems SatisfactoryAccounts NowDelinquentDerog Min. : 0.00 Min. : 2.0 Min. : 1.00 Min. : 0.0000
1st Qu.: 2.00 1st Qu.: 16.0 1st Qu.: 14.00 1st Qu.: 0.0000
Median : 3.00 Median : 24.0 Median : 21.00 Median : 0.0000
Mean : 3.91 Mean : 26.1 Mean : 23.34 Mean : 0.4119
3rd Qu.: 5.00 3rd Qu.: 34.0 3rd Qu.: 30.25 3rd Qu.: 0.0000
Max. :36.00 Max. :115.0 Max. :113.00 Max. :21.0000

WasDelinquentDerog OldestTradeOpenDate DelinquenciesOver30Days Min. : 0.000 Min. : 1011957 Min. : 0.000
1st Qu.: 0.000 1st Qu.: 4101996 1st Qu.: 0.000
Median : 1.000 Median : 7191993 Median : 1.000
Mean : 2.343 Mean : 6934230 Mean : 4.332
3rd Qu.: 3.000 3rd Qu.:10011990 3rd Qu.: 5.000
Max. :32.000 Max. :12312004 Max. :99.000

DelinquenciesOver60Days DelinquenciesOver90Days IsHomeowner LoanStatus Min. : 0.000 Min. : 0.000 Mode :logical 0:1518
1st Qu.: 0.000 1st Qu.: 0.000 FALSE:4264 1:7062
Median : 0.000 Median : 0.000 TRUE :4316
Mean : 1.908 Mean : 4.009 NA's :0
3rd Qu.: 2.000 3rd Qu.: 3.000
Max. :73.000 Max. :99.000

I didn't find any missing values:

try(na.fail(credit))

dput(head(credit,4))

structure(list(ListingCategory = c(1L, 7L, 3L, 1L), IncomeRange = c(3L, 
4L, 6L, 4L), StatedMonthlyIncome = c(2583.3333, 4326, 10500, 
4166.6667), IncomeVerifiable = c(TRUE, TRUE, TRUE, FALSE), DTIwProsperLoan = c(0.18, 
0.2, 0.17, 1e+06), EmploymentStatusDescription = structure(c(1L, 
4L, 1L, 7L), .Label = c("Employed", "Full-time", "Not employed", 
"Other", "Part-time", "Retired", "Self-employed"), class = "factor"), 
    MonthsEmployed = c(4L, 44L, 159L, 67L), BorrowerState = structure(c(22L, 
    32L, 5L, 5L), .Label = c("AK", "AL", "AR", "AZ", "CA", "CO", 
    "CT", "DC", "DE", "FL", "GA", "HI", "ID", "IL", "IN", "KS", 
    "KY", "LA", "MA", "MD", "MI", "MN", "MO", "MS", "MT", "NC", 
    "NE", "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA", 
    "RI", "SC", "SD", "TN", "TX", "UT", "VA", "VT", "WA", "WI", 
    "WV", "WY"), class = "factor"), LenderIndicator = c(0L, 0L, 
    0L, 1L), GroupIndicator = c(FALSE, FALSE, FALSE, TRUE), ChannelCode = c(90000L, 
    90000L, 90000L, 80000L), MonthlyDebt = c(247L, 785L, 1631L, 
    817L), CurrentDelinquencies = c(0L, 0L, 0L, 0L), DelinquenciesLast7Years = c(0L, 
    10L, 0L, 0L), PublicRecordsLast10Years = c(0L, 1L, 0L, 0L
    ), PublicRecordsLast12Months = c(0L, 0L, 0L, 0L), CreditLinesLast7Years = c(53L, 
    30L, 36L, 26L), InquiriesLast6Months = c(2L, 8L, 5L, 0L), 
    AmountDelinquent = c(0L, 0L, 0L, 0L), CurrentCreditLines = c(10L, 
    10L, 18L, 10L), OpenCreditLines = c(9L, 10L, 15L, 8L), BankcardUtilization = c(0.26, 
    0.69, 0.94, 0.69), TotalOpenRevolvingAccounts = c(9L, 7L, 
    12L, 10L), InstallmentBalance = c(48648L, 14827L, 0L, 0L), 
    RealEstateBalance = c(0L, 0L, 577745L, 0L), RevolvingBalance = c(5265L, 
    9967L, 94966L, 50511L), RealEstatePayment = c(0L, 0L, 4159L, 
    0L), RevolvingAvailablePercent = c(78L, 52L, 36L, 45L), TotalInquiries = c(8L, 
    11L, 15L, 2L), TotalTradeItems = c(53L, 30L, 36L, 26L), SatisfactoryAccounts = c(52L, 
    23L, 36L, 26L), NowDelinquentDerog = c(0L, 0L, 0L, 0L), WasDelinquentDerog = c(1L, 
    7L, 0L, 0L), OldestTradeOpenDate = c(5092001L, 5011977L, 
    12011984L, 4272000L), DelinquenciesOver30Days = c(0L, 6L, 
    0L, 0L), DelinquenciesOver60Days = c(0L, 4L, 0L, 0L), DelinquenciesOver90Days = c(0L, 
    10L, 0L, 0L), IsHomeowner = c(FALSE, FALSE, TRUE, FALSE), 
    LoanStatus = structure(c(2L, 1L, 1L, 2L), .Label = c("0", 
    "1"), class = "factor")), .Names = c("ListingCategory", "IncomeRange", 
"StatedMonthlyIncome", "IncomeVerifiable", "DTIwProsperLoan", 
"EmploymentStatusDescription", "MonthsEmployed", "BorrowerState", 
"LenderIndicator", "GroupIndicator", "ChannelCode", "MonthlyDebt", 
"CurrentDelinquencies", "DelinquenciesLast7Years", "PublicRecordsLast10Years", 
"PublicRecordsLast12Months", "CreditLinesLast7Years", "InquiriesLast6Months", 
"AmountDelinquent", "CurrentCreditLines", "OpenCreditLines", 
"BankcardUtilization", "TotalOpenRevolvingAccounts", "InstallmentBalance", 
"RealEstateBalance", "RevolvingBalance", "RealEstatePayment", 
"RevolvingAvailablePercent", "TotalInquiries", "TotalTradeItems", 
"SatisfactoryAccounts", "NowDelinquentDerog", "WasDelinquentDerog", 
"OldestTradeOpenDate", "DelinquenciesOver30Days", "DelinquenciesOver60Days", 
"DelinquenciesOver90Days", "IsHomeowner", "LoanStatus"), row.names = c(NA, 
4L), class = "data.frame")

Any ideas on what's wrong?

Warning message:
In train.default(x, y, weights = w, ...): The metric "Accuracy" was not in the result set. ROC will be used instead.
# weights:  72 (71 variable)
initial  value 5144.538374 
iter  10 value 3540.667624
iter  20 value 3329.692768
iter  30 value 3279.191024
iter  40 value 3264.926986
iter  50 value 3259.276647
iter  60 value 3259.056261
final  value 3259.032668 
converged
# weights:  72 (71 variable)
initial  value 5144.538374 
iter  10 value 3540.774666
iter  20 value 3330.016829
iter  30 value 3279.545595
iter  40 value 3265.384385
iter  50 value 3259.499032
iter  60 value 3259.353010
final  value 3259.342601 
converged
# weights:  72 (71 variable)
initial  value 5144.538374 
iter  10 value 3540.667731
iter  20 value 3329.693092
iter  30 value 3279.191379
iter  40 value 3264.927427
iter  50 value 3259.276899
iter  60 value 3259.056561
final  value 3259.032978 
converged
# weights:  72 (71 variable)
initial  value 5144.538374 
iter  10 value 3528.401458
iter  20 value 3314.932958
iter  30 value 3264.117072
iter  40 value 3253.780051
iter  50 value 3253.368959
iter  60 value 3253.359047
final  value 3253.358819 
converged
# weights:  72 (71 variable)
initial  value 5144.538374 
iter  10 value 3528.508505
iter  20 value 3315.134599
iter  30 value 3265.021404
iter  40 value 3255.739021
iter  50 value 3253.817833
iter  60 value 3253.697180
final  value 3253.671003 
converged
# weights:  72 (71 variable)
initial  value 5144.538374 
iter  10 value 3528.401565
iter  20 value 3314.933160
iter  30 value 3264.117768
iter  40 value 3253.780539
iter  50 value 3253.369030
iter  60 value 3253.359358
final  value 3253.359133 
converged
# weights:  71 (70 variable)
initial  value 5145.231521 
iter  10 value 4680.326236
iter  20 value 4672.506024
iter  30 value 3662.998233
iter  40 value 3310.207744
iter  50 value 3252.983656
iter  60 value 3250.400275
iter  70 value 3250.339216
final  value 3250.332646 
converged

... # weights: 72 (71 variable) initial value 5144.538374 iter 10 value 4661.569290 iter 20 value 4652.246624 iter 30 value 3715.472355 iter 40 value 3484.096833 iter 50 value 3254.247424 iter 60 value 3248.931841 iter 70 value 3248.154679 iter 80 value 3248.129089 iter 80 value 3248.129085 final value 3248.128574 converged # weights: 72 (71 variable) initial value 5144.538374 iter 10 value 4663.660886 iter 20 value 4654.255466 iter 30 value 3542.473235 iter 40 value 3315.027437 iter 50 value 3250.340679 iter 60 value 3248.693378 iter 70 value 3248.455840 iter 80 value 3248.443345 iter 80 value 3248.443325 iter 80 value 3248.443325 final value 3248.443325 converged # weights: 72 (71 variable) initial value 5144.538374 iter 10 value 4661.571382 iter 20 value 4652.248711 iter 30 value 4397.069608 iter 40 value 3532.067046 iter 50 value 3283.179445 iter 60 value 3249.518694 iter 70 value 3248.163057 iter 80 value 3248.129552 final value 3248.128889 converged Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures. Something is wrong; all the ROC metric values are missing: ROC Sens Spec
Min. : NA Min. :0.01805 Min. :0.9946
1st Qu.: NA 1st Qu.:0.01805 1st Qu.:0.9946
Median : NA Median :0.01805 Median :0.9946
Mean :NaN Mean :0.01805 Mean :0.9946
3rd Qu.: NA 3rd Qu.:0.01805 3rd Qu.:0.9946
Max. : NA Max. :0.01805 Max. :0.9946
NA's :3
Error in train.default(x, y, weights = w, ...): Stopping

summaryFunction = twoClassSummary appears to trigger the warning. It happens here as well:

ctrl <- trainControl(method = "cv", summaryFunction = twoClassSummary)

multinomSummaryFit <- train(LoanStatus~., credit, method = "multinom", family=binomial, 
    trControl = ctrl)

Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
Something is wrong; all the ROC metric values are missing:
      ROC           Sens              Spec       
 Min.   : NA   Min.   :0.01919   Min.   :0.9941  
 1st Qu.: NA   1st Qu.:0.01988   1st Qu.:0.9942  
 Median : NA   Median :0.02056   Median :0.9943  
 Mean   :NaN   Mean   :0.02011   Mean   :0.9943  
 3rd Qu.: NA   3rd Qu.:0.02056   3rd Qu.:0.9943  
 Max.   : NA   Max.   :0.02057   Max.   :0.9944  
 NA's   :3                                       
Error in train.default(x, y, weights = w, ...): Stopping

Answer 1

Looking at the output of summary(credit), I can see that there are NA values in at least two variables;

The variable MonthsEmployed has 5 NA values:

MonthsEmployed 
Min.   :-23.00  
1st Qu.: 26.00  
Median : 68.00 
Mean   : 97.44  
3rd Qu.:139.00  
Max.   :755.00  
NA's   :5  

and the variable InstallmentBalance has 328 NA values.

InstallmentBalance
Min.   :     0  
1st Qu.:  3338       
Median : 14453       
Mean   : 24900       
3rd Qu.: 32238      
Max.   :739371    
NA's   :328     

Try removing the rows with missing values (or temporary remove these two variables) and run the function again to see if this solves your problem.

Also, You need to add metric = "ROC" to the train function and classProbs = TRUE to trainControl() when you use twoClassSummary

ctrl <- trainControl(method = "repeatedcv", 
                     repeats = 3, 
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary) . 

So, your call should be

multinomSummaryFit <- train(LoanStatus~., 
                            data = credit, 
                            method = "multinom", 
                            family=binomial, 
                            metric = "ROC",
                            trControl = ctrl)

Another important issue about your dataset, you need to carefully inspect variables' values and make sure that each value makes sense. For example, the MonthsEmployed variable has negative values. Logically, an employee has a positive number of months employed. Are these negative values wrong or do they mean something else! (for example a value of -23 means the person has not been employed for 23 month).

To answer your question regarding confusionMatrix:

Let's say your trained model is called multinomSummaryFit. In order to evaluate your model on the test dataset, you need to call predict method on the test dataset without LoanStatus (using the same variables you trained your model on), and then compare your model predictions to the actual value in LoanStatus. For example,

#let's say your test datafrme is called test
mymodel_pred <- predict(multinomSummaryFit, test[, names(test) != "LoanStatus"])

then use confusionMatrix:

confusionMatrix(data = mymodel_pred, 
                reference = test$LoanStatus, 
                positive = "Default")

If the test dataset does not have the LoanStatus column then you just use:

mymodel_pred <- predict(multinomSummaryFit, test)

but in this case, you have no way to evaluate your model on the test dataset if you do not know the actual response.

Remember, if you removed any variables from the training dataset, you need to remove them also from the test dataset before you call predict

Splitting the data to train and test using stratified sampling:

trainingRows <- createDataPartition(credit$LoanStatus, p = .70, list= FALSE)
train <- credit[trainingRows, ]
test <- credit[-trainingRows, ]

Answer 2

Try to change class variable values from "0","1" to e.g. "A" , "B" and try then.