# Confusion Matrix Results Issue

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Mr X on 27 Dec 2014
Commented: Shoaibur Rahman on 28 Dec 2014
I need to determine the misclassified rate of a machine learning algorithm. However, when I use the confusion function over the actual dataset and the predicted dataset (using the algorithm) the error rate is 0 whereas when I iterate through each element and compare them the error rate is 33.3%. What is wrong with the confusion matrix?
outputs = [1, 1, 1, 100, 10, 100];
predictedOutput = [1, 1, 1, 10,100, 100];
[c,cm] = confusion(outputs,predictedOutput);
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c));
counter =0;
for i = 1: size (predictedOutput,2)
if (predictedOutput(1,i) ~= outputs(1,i))
counter = counter + 1;
end
end
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-(counter/(size(predictedOutput,2)))));

Shoaibur Rahman on 27 Dec 2014
The input arguments of confusion, (in this case, outputs and predictedOutput) should be in range of [0 1]. So, instead of 100 and 10, use 1 and 0, for example.
outputs = [1, 1, 1, 1, 0, 1];
predictedOutput = [1, 1, 1, 0, 1, 1];
[c,cm] = confusion(outputs,predictedOutput);
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c));
counter =0;
for i = 1: size (predictedOutput,2)
if (predictedOutput(1,i) ~= outputs(1,i))
counter = counter + 1;
end
end
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-(counter/(size(predictedOutput,2)))));
##### 2 CommentsShowHide 1 older comment
Shoaibur Rahman on 28 Dec 2014
That could be one way, given it serves your purpose. Another way is to analyze the confusion matrix (user your original outputs,predictedOutput).
cmat = confusionmat(outputs,predictedOutput);
This will allow you to determine the misclassification for each group separately.

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