how to compare a vector with a column of a matrix?

I designed a simple decision tree to work on the Iris data set, with two values for input: petal width and petal length, and one output: versicolor or virginica (only these samples were taken) I want to measure the accuracy of each class and for that I am trying to compare the output of my tree to the species column of the iris dataset. Please let me know what changes can I do to the code:

Totalsamples  = size(Petal_length,1);
predictedclass=[];
for i=51:Totalsamples
    if Petal_length(i) < 4.8 && Petal_width(i) < 1.7
        clspred = 2;
    else 
        clspred = 3;
    end
   predictedclass = [predictedclass; clspred];
end
truepred2 = [];
for j = 1:50
    if predictedclass(j) == 2 
        tpred = 2;
    end
    truepred2 = [truepred2; tpred];
end
truepred3 = [];
for l = 51:100
    if predictedclass(l) == 3
        tpred3 = 3;
    end
    truepred3 = [truepred3; tpred3];
end
Reqdsamples = [versicolor; virginica];
truelabel = Reqdsamples(:,5);
cpt = classperf(truelabel,predictedclass);
accuracy_for_versicolor = size(truepred2,1)/size(versicolor,5);
accuracy_for_virginica = size(truepred3,1)/size(virginica,5);
disp(cpt.correctrate)

[Kevin Phung]

any chance you can provide numerical examples to help convey your question?

'Please let me know what changes can I do to the code:'

are you asking if your code can be improved, or are you trying to do something that you do not know how to do? or both?

[Apurva Jariwala]

I am trying to find the accuracy of the classification for versicolor and virginica classes individually. The code that I used is not able to do so. I tried using a for loop but it was still not working.

I just need to find the accuracy individually for each class. I need some help doing that

Answer 1

[Apurva Jariwala]

Totalsamples  = size(Petal_length,1);
predictedclass=[];
for i=51:Totalsamples
    if Petal_length(i) < 4.8 && Petal_width(i) < 1.7
        clspred = 2;
    else 
        clspred = 3;
    end
   predictedclass = [predictedclass; clspred];
end
Reqdsamples = [versicolor; virginica];
truelabel = Reqdsamples(:,5);
cpt = classperf(truelabel,predictedclass);
cmat = confusionmat(truelabel,predictedclass);
trueclas=[];
trueclas = diag(cmat);
Nos = size(truelabel, 1)/2;
accuracy_for_versicolor = trueclas(1)/Nos;
accuracy_for_virginica = trueclas(2)/Nos;
disp(cpt.correctrate)