What term describes the frequency with which a machine learning model correctly identifies all actual negative instances?

The term that describes the frequency with which a machine learning model correctly identifies all actual negative instances is specificity. Specificity measures the proportion of true negatives correctly identified by the model out of the total actual negatives. It essentially indicates how well the model can avoid false positives, focusing specifically on the negative class in a binary classification context.

In practice, a high specificity means that the model is effective in recognizing the instances that do not belong to the positive class, thus reducing the likelihood of incorrectly classifying negative instances as positive. This is particularly important in applications where false positives can lead to significant consequences, such as in medical diagnostics where falsely identifying a healthy patient as having a disease can cause undue stress and unnecessary treatment.

The other terms are not applicable in this scenario. Recall measures the ability to identify all actual positive instances, precision indicates the proportion of true positives among all predicted positives, and accuracy reflects the overall correctness of the model in classifying both positive and negative instances. Understanding specificity is crucial for evaluating models in scenarios where accurately distinguishing between negative instances is essential.