The phrase "a plot of the sensitivity" is correct and usable in written English.
It can be used in contexts related to data analysis, statistics, or scientific research where sensitivity is being visualized or represented graphically.
Example: "In our study, we created a plot of the sensitivity to illustrate how the variable changes with different parameters."
Alternatives: "a graph of the sensitivity" or "a chart of the sensitivity".
The ROC curve is a plot of the sensitivity versus specificity for the different possible cut-points of a diagnostic test.
The ROC curve is a plot of the sensitivity (or true-positive rate) to the false-positive rate [31].
AUC is a threshold independent measure, and was calculated from the ROC curve which is a plot of the sensitivity against the False Positive rate = FP/ FP + TN).
Using this method, a plot of the sensitivity and false positive rate (1-specificity) for discriminating between survivors and non-survivors was assessed.
The ROC curve is a plot of the sensitivity versus [1-specificity] over all possible threshold values of the test being validated.
A plot of the sensitivity against 1-specificity for all possible choices of T is known as a receiver-operating characteristic (ROC) curve (9).
The Receiver Operating Characteristics (ROC) curve is a plot of the balance between sensitivity and specificity for a diagnostic test [ 28].
The ROC curve (a plot of the true-positive rate (sensitivity) against the false-positive rate (100-specificity) that is obtained at each cutoff point) was constructed, and the area under the curve (AUC) value was calculated as a measure of the accuracy of the test.
The ROC-curve is a plot of the true positive rate (sensitivity) against the false-positive rate (1-specificity) of the model.
To test whether the combined model of common and rare variation had clinical utility for obesity risk prediction, we assessed diagnostic efficiency by calculating the area under the (AUC) receiver operator criteria (ROC) curves, which is a plot of the true positive rate (sensitivity) against the false positive rate (1 - specificity).
A ROC curve is a graphical plot of the sensitivity vs. (1 - specificity) for a binary classifier system as its discrimination threshold is varied.
