Which of the following is a key metric for evaluating classification models?

Accuracy is a fundamental metric for evaluating classification models as it measures the proportion of correct predictions made by the model relative to the total number of predictions. Specifically, it calculates the ratio of true positive and true negative predictions to the overall number of instances in the dataset. This metric provides a straightforward understanding of how well the model performs in classifying instances into their respective categories.

In classification problems, particularly when the classes are balanced, accuracy can effectively communicate the model's reliability. However, it is important to note that in cases of class imbalance or specific business requirements, additional metrics may also be necessary to gain a comprehensive view of a model's performance.