How to calculate the accuracy of the classifier ? and show the confusion matrix.

I had implemented a simple face recognition code. how to calculate its accuracy? and how to plot the confusion matrix .

clc
clear

% Load Image dataset
faceDatabase = imageSet('facedatabaseatt','recursive');

%splitting into training and testing sets

[training,test] = partition(faceDatabase,[0.8 0.2]);

% Extract HOG Features for training set 
featureCount = 1;
for i=1:size(training,2)
    for j = 1:training(i).Count
        trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j));
       % imshow(read(training(i),j));
        %pause(0.0011);
        trainingLabel{featureCount} = training(i).Description;    
        featureCount = featureCount + 1;
    end
    personIndex{i} = training(i).Description;
end

% Create 40 class classifier
faceClassifier = fitcknn(trainingFeatures,trainingLabel);

%testing

for person=1:40
    for j = 1:test(person).Count
        queryImage = read(test(person),j);
        queryFeatures = extractHOGFeatures(queryImage);
        actualLabel = predict(faceClassifier,queryFeatures) %actuallabel
        C=test(person).Description;                           % predictedlabel
        predictedLabel= cellstr(C)           %converting into cell array

        
    end

end

 

Answer 1

[Hitham]

confusionmat computes the Confusion matrix as.

[cm,order] = confusionmat(actualLabel,predictedLabel);

Or, you can use my function.

function [c_matrix]=confusionmat1(actual,predict)

% It computes confusion matrix

classList=unique(actual);

N=length(classList);

cm = zeros(N,N);

for j=1:length(actual)

posClassGT = strmatch(actual{j}, classList, 'exact');

posClass = strmatch(predict{j}, classList, 'exact');

cm(posClassGT,posClass) = cm(posClassGT,posClass) + 1;

end

end