What measures the fraction of cases where the decision matches the actual target value?

The measure that assesses the fraction of cases where the decision matches the actual target value is accuracy. Accuracy is calculated by taking the number of correct predictions (both true positives and true negatives) and dividing it by the total number of cases evaluated. It provides a straightforward indication of how well the model performs overall in predicting the correct labels for the data.

While other metrics like misclassification, precision, and F1 Score relate to model performance, they serve different purposes. Misclassification refers to the count of incorrect predictions but does not provide a ratio or fraction of correctly classified items. Precision measures the accuracy of positive predictions by calculating the proportion of true positives relative to the total predicted positives, which does not account for all cases. The F1 Score combines precision and recall to provide a single score that balances both metrics but does not directly reflect overall classification accuracy. Therefore, accuracy is the most direct and appropriate measure of the fraction of correct decisions made by the model compared to the actual target value.