Classification confusion matrix
[c,cm,ind,per] = confusion(targets,outputs)
[c,cm,ind,per] = confusion(targets,outputs)
takes
these values:
targets 

outputs 

and returns these values:
c  Confusion value = fraction of samples misclassified 
cm 

ind 

per 
per(i,1) false negative rate = (false negatives)/(all output negatives) per(i,2) false positive rate = (false positives)/(all output positives) per(i,3) true positive rate = (true positives)/(all output positives) per(i,4) true negative rate = (true negatives)/(all output negatives) 
[c,cm,ind,per] = confusion(TARGETS,OUTPUTS)
takes
these values:
targets 

outputs 

and returns these values:
c  Confusion value = fraction of samples misclassified 
cm 

ind 

per 

[x,t] = simpleclass_dataset; net = patternnet(10); net = train(net,x,t); y = net(x); [c,cm,ind,per] = confusion(t,y)