Hi @Alyaa,
I would use the predict function to make predictions on new data and then evaluate the model's performance using various metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Please click the link below to find out more about using this function,
https://www.mathworks.com/help/stats/linearmodel.predict.html
Here is example code snippet,
% Load your trained model
load('trainedModel.mat');
% Make predictions on new data
predictions = predict(trainedModel, newData);
% Evaluate the model
metrics = confusionmat(newDataLabels, predictions);
accuracy = sum(diag(metrics)) / sum(metrics, 'all');
precision = metrics(2,2) / sum(metrics(:,2));
recall = metrics(2,2) / sum(metrics(2,:));
f1Score = 2 * (precision * recall) / (precision + recall);
disp(['Accuracy: ', num2str(accuracy)]);
disp(['Precision: ', num2str(precision)]);
disp(['Recall: ', num2str(recall)]);
disp(['F1-Score: ', num2str(f1Score)]);
disp('Confusion Matrix:');
disp(metrics);
So, this example code snippet first loads a pre-trained model from a file named 'trainedModel.mat'. It then uses this model to make predictions on new data, calculating metrics like accuracy, precision, recall, and F1-score based on the predictions and the true labels of the new data. Finally, it displays these metrics along with the confusion matrix to assess the model's performance.
Hope this answers your question, please let me know if you have any further questions.
