Evaluating Multi-Class Image Classification

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srinivas talasila
srinivas talasila 2022-1-19
编辑： srinivas talasila 2022-1-19
Here I written a code for evaluating multi class image classification. Kindly correct if I'm wrong
C=confusionmat(Yactual,YPred)
[row,col]= size(C);
n_class=row;
for i=1:n_class
TP(i)=C(i,i);
FN(i)=sum(C(i,:))-C(i,i);
FP(i)=sum(C(:,i))-C(i,i);
TN(i)=sum(C(:))-TP(i)-FP(i)-FN(i);
end
TP1=sum(TP)
FP1=sum(FP)
FN1=sum(FN)
TN1=sum(TN)
Accuracy=(TP1+TN1)/(TP1+TN1+FP1+FN1)
Error=1-Acc
Recall=TP1/(TP1+FN1)
Precision=TP1/(TP1+FP1)
Specificity = TN1/(TN1+FP1)
Sensitivity = TP1/(TP1+FN1)
FPR=1-Specificity
beta=1;
F1_score=( (1+(beta^2))*(Recall.*Precision)) ./ ( (beta^2)*(Precision+Recall))
I want to know above code is correct or not?
and also when I'm using accuracy = mean(YPred == Yactual) it gives precision value. why?
kindly help me in this regard. Thank you.

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