MATLAB: How can i calculate the percentage of error

classificationwine

I have this code:
%%Loading data
load('wine.data');
% first column stores the wine class according to wine.names file
nClass=max(wine(:,1)); 
%%Getting the mean of each class for the 13 parameters
meanEachClass=arrayfun(@(x) mean( wine( wine(:,1)==x ,2:end) ), 1:nClass,'UniformOutput',false);
%%Now checking the euclidean distance of a sample 
% relative to the mean of each class
nSampleToTest=10;
for i=1:nSampleToTest
  % Randomly choosing a sample
  sampleNo=randi(size(wine,1));
  sample=wine(sampleNo,2:end);
    % calculate the Eudlidian distance to each class.
    distances=arrayfun(@(x) norm(sample-meanEachClass{x}), 1:nClass, 'UniformOutput',true);
    disp(sprintf('Sample #%d',sampleNo))
    disp(sprintf('Distance: \n   Class 1: %f \n   Class 2: %f \n   Class 3: %f \n',distances(1),distances(2),distances(3)));
    disp(sprintf('Based on distance, Sample seems to belong to class %d\n', find(distances==min(distances))))
    disp(sprintf('According to the database, sample belongs to class %d\n',wine(sampleNo,1)))
  end
Here is the database: http://archive.ics.uci.edu/ml/machine-learning-databases/wine/ I found that some of the random vectors chosen are belonging to the class 2 for example and the algorithm points to class 1. How can i calculate the percentage of the algorithm precision ? Can you provide me some code?

Best Answer

You need to look at what class you think it is and compare it to what class it actually is:
Before the loop:
truePositives = zeros(1, nSampleToTest); % Initialize.
Inside the loop:
[~, myClass] = find(distances==min(distances));
trueClass = wine(sampleNo,1);
if myClass == trueClass
     % Accurately determined the class.
     truePositives(sampleNo) = 1
end
After the loop:
overallAccuracyPercentage = 100 * sum(truePositives) / nSampleToTest;
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