A binary classification model labels products as "Passed" or "Failed" and sorts items accordingly on an assembly line. Which evaluation metrics should be used to assess the model's performance? (Choose two.)

For a binary classification model like this, where misclassifications have different implications, Precision, Recall, Accuracy, and F1-score are crucial. Precision measures the proportion of correctly identified "Failed" products out of all products the model labeled as "Failed" (minimizing false positives). Recall measures the proportion of correctly identified "Failed" products out of all actual "Failed" products (minimizing false negatives). The F1-score is the harmonic mean of precision and recall, providing a balanced measure, especially useful when there's an uneven class distribution. Accuracy measures overall correct predictions but can be misleading if one class is much more prevalent. RMSE and MAPE are regression metrics, not suitable for classification. BLEU score and Perplexity are used for natural language processing tasks.