Society of Actuaries (SOA) PA Practice Exam

What does specificity indicate in predictive modeling?

1. TPR = TP / (TP + FN)

2. FNR = FN / (FN + TN)

3. TNR = TN / (TN + FP)

4. Precision = TP / (TP + FP)

The correct answer is: TNR = TN / (TN + FP)

Specificity is a key metric in predictive modeling, particularly in the context of binary classification tasks. It specifically measures the true negative rate, indicating the proportion of actual negatives that are correctly identified by the model. When looking at the formula associated with specificity, it is defined as true negatives divided by the sum of true negatives and false positives. This means that specificity focuses on the instances where the outcome is actually negative and assesses how many of those were correctly predicted by the model. A high specificity indicates that the model is effective at identifying negative cases, thereby minimizing false positives. The other formulas detailed in the options pertain to different aspects of model performance. The formula for true positive rate (sensitivity) relates to identifying actual positives. The false negative rate addresses the proportion of actual positives that were incorrectly classified as negatives. Lastly, precision measures the accuracy of positive predictions but does not reflect the model’s ability to correctly identify negatives. This distinct focus on negative results is what makes specificity an essential measure in evaluating the effectiveness of predictive models.