Hi Kanuparthi,
You can compute the accuracy by comparing the predicted labels (“yfit”) with the actual labels (say “yTest”).
The accuracy is calculated as the percentage of correctly predicted labels. Below is a sample MATLAB code for the same:
% Assuming yTest contains the actual labels of the test data
correctPredictions = sum(yfit == yTest); % Count correct predictions
totalTestSamples = length(yTest); % Total number of test samples
% Compute accuracy
accuracy = (correctPredictions / totalTestSamples) * 100;
% Display accuracy
fprintf('Test Accuracy: %.2f%%\n', accuracy);
This will give you the classification accuracy of the model on the test dataset. If your labels are categorical, you may need to use “categorical(yfit) == categorical(yTest)” for comparison.
For further reference, you can refer to the following official MATLAB documentation:
web(fullfile(docroot, 'stats/select-data-and-validation-for-classification-problem.html'))
