The curve that illustrates the trade-off between sensitivity and false positive fraction is known as what?

The correct choice refers to the Receiver Operating Characteristic (ROC) curve, a critical tool in evaluating the performance of binary classification models. The ROC curve illustrates the relationship between sensitivity (true positive rate) and the false positive fraction (1 - specificity).

As the threshold for classifying instances as positive is varied, the ROC curve plots the true positive rate against the false positive rate, effectively showing the trade-offs between the two rates. This is essential for understanding how well a classifier can distinguish between the positive and negative classes under various settings. A classifier that scores well will have an ROC curve that bows towards the upper left corner of the plot, indicating high sensitivity and low false positive rates.

This curve is particularly valuable because it provides insights not just at a single decision threshold but across all possible thresholds, enabling a more comprehensive evaluation of model performance. In contrast, the other options do not address the trade-off between sensitivity and false positive rates in the context of classification performance, making them less relevant in this scenario.