Manually review the model's predictions for each test sample and provide feedback on its accuracy.

Use a simple accuracy metric to calculate the percentage of correct predictions out of the total test samples.

Measure the model's performance using a combination of precision, recall, and F1-score metrics, and analyze the confusion matrix to identify areas for improvement.

Test the model's performance by deploying it in a production environment and collecting user feedback.