Confusion matrix error with less test data than training data - matlab

Question

I have an issue with my model accuracy calculation. I used the code below:
y_train = [ 1 1 1 4 4 3 3 5 5 5 ]; % true labels for x_train
%x_test : has no true labels.
predictedLabel=[ 1 2 3 4 5 ]; % predicted labels for x_test
group=y_train ; % 10
grouphat=predictedLabel; % for test 5 test data
C=confusionmat(group,grouphat);
Accuracy = sum ( diag (C)) / sum (C (:)) ×100;
but I get the error:
Error using confusionmat (line 75)
G and GHAT need to have same number of rows
Do I get this error since the test data is more or less than the train? There is no true label for test data (semi supervised learning).

Answer 1

Your training labels and predicted labels are based on different inputs, so it doesn't make sense to compare them in a confusion matrix. From the confusionmat docs:
returns the confusion matrix C determined by the known and predicted groups
i.e. the known and predicted results for the same data.
Take this partly pseudo-code example, see the comments for details
% split your input data
trainData = data(1:100, :); % Training data
testData = data(101:120, :); % Testing data (mutually exclusive from training)
% Do some training (pseudo-code, not valid MATLAB)
% ** Let's assume that the labels are in column 1 **
model = train( trainData(:,1), trainData(:,2:end) );
% Test your model on the input data, excluding the actual labels in column 1
predictedLabels = model( testData(:,2:end) );
% Get the actual labels from column 1
actualLabels = testData(:,1);
% Note that size(predictedLabels) == size(actualLabels)
% Now we can do a confusion matrix
C = confusionmat( actualLabels, predictedLabels )