One Class Classification in R language. What am I doing wrong when generating the confusion matrix?

[Hugo_Souza]

I am trying to understand and implement classifiers A class in R is based on several UCIs and one of them (http://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease).

When trying to print a confusion matrix you are giving the error “all arguments must have the same length”.

What am I doing wrong?

library(caret)
library(dplyr)
library(e1071)
library(NLP)
library(tm)

ds = read.csv('kidney_disease.csv', 
              header = TRUE)

#Remover colunas inutiliz?veis              
ds <- subset(ds, select = -c(age), classification =='ckd' )

x <- subset(ds, select = -classification) #make x variables
y <- ds$classification #make y variable(dependent)

# test on the whole set
#pred <- predict(model, subset(ds, select=-classification))


trainPositive<-x
testnegative<-y

inTrain<-createDataPartition(1:nrow(trainPositive),p=0.6,list=FALSE)

trainpredictors<-trainPositive[inTrain,1:4]
trainLabels<-trainPositive[inTrain,6]

testPositive<-trainPositive[-inTrain,]
testPosNeg<-rbind(testPositive,testnegative)

testpredictors<-testPosNeg[,1:4]
testLabels<-testPosNeg[,6]

svm.model<-svm(trainpredictors,y=NULL,
               type='one-classification',
               nu=0.10,
               scale=TRUE,
               kernel="radial")

svm.predtrain<-predict(svm.model,trainpredictors)
svm.predtest<-predict(svm.model,testpredictors)

# confusionMatrixTable<-table(Predicted=svm.pred,Reference=testLabels)
# confusionMatrix(confusionMatrixTable,positive='TRUE')

confTrain <- table(Predicted=svm.predtrain,Reference=trainLabels)
confTest <- table(Predicted=svm.predtest,Reference=testLabels)

confusionMatrix(confTest,positive='TRUE')


print(confTrain)
print(confTest)

#grid

Here are some of the first lines of the dataset I'm using:

 id bp    sg al su    rbc       pc        pcc         ba bgr bu  sc sod pot hemo pcv   wc
1  0 80 1.020  1  0          normal notpresent notpresent 121 36 1.2  NA  NA 15.4  44 7800
2  1 50 1.020  4  0          normal notpresent notpresent  NA 18 0.8  NA  NA 11.3  38 6000
3  2 80 1.010  2  3 normal   normal notpresent notpresent 423 53 1.8  NA  NA  9.6  31 7500
4  3 70 1.005  4  0 normal abnormal    present notpresent 117 56 3.8 111 2.5 11.2  32 6700
5  4 80 1.010  2  0 normal   normal notpresent notpresent 106 26 1.4  NA  NA 11.6  35 7300
6  5 90 1.015  3  0                 notpresent notpresent  74 25 1.1 142 3.2 12.2  39 7800
   rc htn  dm cad appet  pe ane classification
1 5.2 yes yes  no  good  no  no            ckd
2      no  no  no  good  no  no            ckd
3      no yes  no  poor  no yes            ckd
4 3.9 yes  no  no  poor yes yes            ckd
5 4.6  no  no  no  good  no  no            ckd
6 4.4 yes yes  no  good yes  no            ckd

The error log:

> confTrain <- table (Predicted = svm.predtrain, Reference = trainLabels)
Table error (Predicted = svm.predtrain, Reference = trainLabels):
all arguments must be the same length
> confTest <- table (Predicted = svm.predtest, Reference = testLabels)
Table error (expected = svm.predtest, reference = testLabels):
all arguments must be the same length
>
> confusionMatrix (confTest, positive = 'TRUE')
ConfusionMatrix error (confTest, positive = "TRUE"):
'confTest' object not found
>
>
> print (confTrain)
Printing error (confTrain): object 'confTrain' not found
> print (confTest)
Printing error (confTest): object 'confTest' not found

[nirgrahamuk]

What class of object is svm.predtrain? And what are its dimensions?

class(svm.predtrain)
dim(svm.predtrain)

[Hlynur]

This almost certainly has to do with missing data. That is, the svm.predtrain object you're getting from the predict function is of a shorter length (123 rows?) than the trainLabels object (152 rows?) because svm() is not outputting predictions for the rows where data is missing.

[Max]

Maybe try na.exclude:

na.exclude differs from na.omit only in the class of the "na.action" attribute of the result, which is "exclude" . This gives different behaviour in functions making use of naresid and napredict : when na.exclude is used the residuals and predictions are padded to the correct length by inserting NA s for cases omitted by na.exclude .