Which of the following is NOT a metric typically used for classification problems?

Mean Squared Error (MSE) is primarily a metric used for regression problems rather than classification. It measures the average of the squares of the errors, which is the difference between the predicted values and the actual values. In classification tasks, the outcomes are categorical, making MSE inappropriate for evaluating model performance.

In contrast, Accuracy, Precision, and F1 Score are metrics specifically designed for classification. Accuracy calculates the proportion of correctly classified instances among the total instances. Precision assesses the ratio of true positive predictions to the total predicted positives, highlighting the quality of positive predictions. The F1 Score combines both precision and recall into a single metric, serving as a balance between them. Thus, options related to Accuracy, Precision, and F1 Score are all relevant and commonly employed in the context of classification problems, confirming that Mean Squared Error is not a suitable choice here.