The phrase "a perfect discrimination between" is correct and usable in written English.
It can be used when discussing the ability to distinguish or differentiate clearly between two or more things.
Example: "The study aims to achieve a perfect discrimination between the effects of the two medications on patient recovery times."
Alternatives: "a clear distinction between" or "an accurate differentiation between".
Note that even if we assume that the opponent print and scan channel is perfect ( x ̂ N = z N ), because the input of the printer has to be binary and because the opponent will make decoding errors by estimating the original code, the copied printed code will be necessarily different from the original printed code (see Figure 1), which implies a perfect discrimination between the two hypotheses.
An AUC value of 0.5 indicates that the model has no predictive ability, whereas a perfect discrimination between suitable and unsuitable cells will achieve the best possible AUC of 1.0.
From the individual patient's point of view, a perfect discrimination would be preferable; however, for clinical trials or comparison of care between ICUs, better calibration is needed.
A perfect discrimination model that correctly recognizes the subpopulation of each individual, when the hazard ratio between high versus low risk patients is 2.7, achieves on average a C-index of 0.62.
In general, discrimination refers to the ability of a model to distinguish well between individuals with and without incident diabetes; a value of 1 implies a perfect discrimination and a value of 0.5 implies performance no better than chance.
An AUC (area under the curve) of 0.5 indicates the absence of discriminatory ability, and 1 indicates a perfect discrimination [ 32, 33].
Adapting EVORA to the original beta methylation scale to better capture the observed bi-modal methylation profiles yielded a perfect discrimination of normal and cancer tissue.
The extent to which hand posture reflects object shape was calculated using a "visuomotor efficiency (VME) index" (a score of 100 reflects perfect discrimination between objects).
An AUC value of 0.5 corresponds to no predictive value, whereas an AUC of 1 represents perfect discrimination between groups (converters and stable).
Next, we employ the best performing classification schemes identified using each source of data separately, and merge the individual predictions using a weighted majority voting algorithm, achieving perfect discrimination between patients with and without disease relapse.
A random discrimination will give an area of 0.5 under the curve while perfect discrimination between classes will give unity area under the ROC curve.
