An AI practitioner must choose a performance metric that shows the ratio of correctly classified items to the total number of items classified (both correct and incorrect). Which metric is that?

Accuracy is the correct metric because it directly measures the proportion of total predictions that were correct. It is calculated as (True Positives + True Negatives) / (True Positives + True Negatives + False Positives + False Negatives). Precision, on the other hand, focuses only on the positive predictions, calculating the ratio of true positive predictions to the total positive predictions (True Positives / (True Positives + False Positives)). Recall (also known as sensitivity) measures the proportion of actual positives that were correctly identified (True Positives / (True Positives + False Negatives)). The F1 score is the harmonic mean of precision and recall, providing a balanced measure, especially useful when there is an uneven class distribution.