What measures the fraction of cases where the decision does not match the actual target value?

Misclassification refers specifically to the instances in which the predicted results differ from the actual target values in a classification problem. This metric is crucial for understanding how well a predictive model performs, as it quantifies errors made by the model. By assessing the proportion of cases where the model's predictions do not align with the true labels, misclassification provides insight into the model's reliability and effectiveness.

In contrast, accuracy measures the overall proportion of correct predictions (both true positives and true negatives) against the total predictions made, focusing on performance rather than error. Recall, on the other hand, emphasizes the model's ability to identify cases belonging to the positive class, while specificity looks at correctly identifying the negative class. Therefore, while accuracy, recall, and specificity provide valuable metrics for assessing model performance, misclassification directly captures the error aspect, making it the correct choice for measuring the fraction of cases where the decision does not match the actual target value.