Which statement about the ROC Curve best reflects model performance?

Which statement about the ROC Curve best reflects model performance?

• A. The 45-degree line indicates perfect prediction.
• B. The closer the ROC curve hugs the upper left corner, the better the model predicts.
• C. The y-axis represents specificity.
• D. The x-axis represents sensitivity.

Answer: (D)

The ROC curve shows how the true positive rate (sensitivity) changes as you vary the false positive rate (which is 1 minus specificity). A model that discriminates well will have a curve that sits toward the top-left of the plot, meaning you can achieve a high true positive rate with a low false positive rate. Key points to keep straight: the y-axis is sensitivity (true positive rate), not specificity; the x-axis is the false positive rate (1 − specificity), not sensitivity; and the 45-degree line represents random guessing, not perfect prediction. So the best reflection of strong performance is the curve hugging the upper-left corner, indicating high sensitivity with few false positives.

The ROC curve shows how the true positive rate (sensitivity) changes as you vary the false positive rate (which is 1 minus specificity). A model that discriminates well will have a curve that sits toward the top-left of the plot, meaning you can achieve a high true positive rate with a low false positive rate.

Key points to keep straight: the y-axis is sensitivity (true positive rate), not specificity; the x-axis is the false positive rate (1 − specificity), not sensitivity; and the 45-degree line represents random guessing, not perfect prediction. So the best reflection of strong performance is the curve hugging the upper-left corner, indicating high sensitivity with few false positives.