r - Error while computing Confusion matrix - Neuralnet package -

data used (sample)

empn target dailyrate dfrmhom

empnumber	target	dailyrate	dfrmhome	education	employeecount	age	hourlyrate	jobinvolvment	joblevel  1	0	1	1	1	-1	1	1	1	1  2	1	1	1	1	1	1	1	1	1  3	0	1	-1	1	1	-1	1	1	1  4	1	1	1	1	1	1	1	1	1  5	0	1	-1	1	1	-1	1	1	1  6	1	1	-1	1	1	-1	1	-1	1  7	0	1	1	1	-1	1	1	1	1  8	1	1	1	1	1	1	1	1	1  9	1	1	-1	1	1	-1	1	1	1  10	1	1	1	1	1	1	1	1	1  11	0	1	-1	1	-1	-1	1	1	1  12	0	1	1	1	-1	1	1	1	1  13	0	1	1	1	1	1	1	1	1  14	1	1	1	1	1	-1	1	1	1  15	1	1	1	1	1	1	1	1	1  16	1	1	-1	1	1	-1	1	-1	1  17	0	1	1	1	1	-1	1	1	1  18	0	1	-1	1	1	-1	1	1	1  19	0	1	1	1	-1	1	1	1	1  20	0	1	-1	1	1	-1	1	1	1  21	0	1	1	1	-1	1	1	1	1  22	0	1	1	1	-1	1	1	1	1

  rcode :       library(neuralnettools)     #read data train , test data      ctdf = read.csv("project-attrition.csv",header=t,na.strings=c(""))     # let set differnt seeds , extract 70 % of poulation arriving            @  development, test , holdout samples     ctdf.dev  = sample.split(ctdf$target,splitratio=0.70)     head(split,20)     ctdf.train= subset(ctdf, split== true)     str(ctdf.train)     view(ctdf.train)     table(ctdf.train$target)     ctdf.test= subset(ctdf, split== false)     str(ctdf.test)     table(ctdf.test$target)     ctdf.nnd=ctdf.train     ctdf.nnt=ctdf.test     model = =nnet(target~dailyrate+dfrmhome +             +education+employeecount+age+hourlyrate+jobinvolvment+joblevel,              data=ctdf.nnd, size=30, rang= 0.1, decay = 5e-4 , maxit = 500)     table(actual=ctdf.nnd$target, prediction=predict(model, data=ctdf.nnd))     ctdf.nnt$predict.class = predict(model, ctdf.nnt)        confusionmatrix(ctdf.nnt$predict.class, ctdf.nnt$target)  output :    str(ctdf.train)       'data.frame': 3167 obs. of  10 variables:   $ empnumber    : int  1 2 4 5 6 7 11 12 14 15 ...   $ target       : int  0 1 1 0 1 0 0 0 1 1 ...   $ dailyrate    : int  1 1 1 1 1 1 1 1 1 1 ...   $ dfrmhome     : int  1 1 1 -1 -1 1 -1 1 1 1 ...   $ education    : int  1 1 1 1 1 1 1 1 1 1 ...   $ employeecount: int  -1 1 1 1 1 -1 -1 -1 1 1 ...   $ age          : int  1 1 1 -1 -1 1 -1 1 -1 1 ...   $ hourlyrate   : int  1 1 1 1 1 1 1 1 1 1 ...   $ jobinvolvment: int  1 1 1 1 -1 1 1 1 1 1 ...   $ joblevel     : int  1 1 1 1 1 1 1 1 1 1 ... 

for "table(actual=ctdf.nnd$target, prediction=predict(model, data=ctdf.nnd))"

i expecting confusion matrix below.     0    1   0   1 

however have got :

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   prediction 

actual 0.00892725347390036 0.0358546806229366 0.0376897872686173 0.10518235583921 0.124456456913317 0 3 242 1 34 197 1 0 9 0 4 28 prediction actual 0.1363541434416 0.236290782608584 0.286744920923175 0.331511427682813 0.613818492677834 0.726882504157994 0 19 42 5 474 51 6 1 3 13 2 235 81 16 prediction

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also test data computing confusion matrix getting following errors:

   confusionmatrix(ctdf.nnt$predict.class, ctdf.nnt$target) 

error in confusionmatrix.default(ctdf.nnt$predict.class, ctdf.nnt$target) : data cannot have more levels reference

please in overcoming these issues.