how to find the accuracy from the predicted labels for test data in Matlab?

I am using classification learner app svm generated code for the classification of multiclass dataset.

Now I wanted to test with the unseen dataset for this I am using yfit.

Now I got the predicted labels for the test data. How to find the test accuracy and from the predicted laebls?

Can someone please help me in this.

Answer 1

[Riya]

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'))