Sensitivity is defined as

Sensitivity is defined as

• A. Specificity is the probability the model predicts a negative outcome for an observation when in fact the observation is negative.
• B. The ROC Curve plots sensitivity against false positive rate.
• C. Sensitivity is the probability the model predicts a positive outcome for an observation when in fact that observation is positive.
• D. The accuracy measures the fraction of correct predictions.

Answer: (C)

Sensitivity is the true positive rate: the probability that the model predicts a positive outcome given that the observation is actually positive. It tells you how good the test is at identifying positives and is computed as TP / (TP + FN). A high sensitivity means few false negatives, which is crucial when missing a positive is costly. This is different from specificity, which is the probability the model predicts negative when the observation is negative. The ROC curve is drawn with the true positive rate (which is sensitivity) on one axis and the false positive rate on the other axis, highlighting the trade-off between detecting positives and raising false alarms. Accuracy, on the other hand, measures the overall fraction of correct predictions, combining both positives and negatives.

Sensitivity is the true positive rate: the probability that the model predicts a positive outcome given that the observation is actually positive. It tells you how good the test is at identifying positives and is computed as TP / (TP + FN). A high sensitivity means few false negatives, which is crucial when missing a positive is costly. This is different from specificity, which is the probability the model predicts negative when the observation is negative. The ROC curve is drawn with the true positive rate (which is sensitivity) on one axis and the false positive rate on the other axis, highlighting the trade-off between detecting positives and raising false alarms. Accuracy, on the other hand, measures the overall fraction of correct predictions, combining both positives and negatives.