Sentence examples for perfect discriminating from inspiring English sources

The c-statistic can range from 0.5 (discriminating ability equivalent to random chance) to 1.0 (perfect discriminating ability).

The AUC is also known as the C-statistic or C-index, with 1 being a perfect discriminating test and 0.5 having no discriminating value [ 16– 16].

The ROC curve analysis combining the wall thickness of the ileo-colonic anastomosis with the extension of neo-terminal ileum intramural lesions shows a value of 0.95 (i.e., very good, given that 1 means perfect discrimination) in discriminating patients with score 0 from 1-4 and a value of 0.90 (i.e. good) in discriminating patients with score 0 from 1 (figure 4A and 4B).

The area under the curve ranges from 0.50 (no ability to discriminate) to 1 (perfect discrimination).

Although it remains to be seen what parameters influence Td-w, it does out-perform Td in discriminating perfect-match from mismatch probes.

Discriminating perfect-match from mismatch hybridizations – a key step toward determining the presence or absence of a particular target (e.g. species) – is also influenced by such factors as the diffusivity of the gel array, the quality of the target material, and image analysis methods.

A C statistic of 0.5 indicates that the model discriminates no better than chance alone, whereas a value of 1.0 indicates perfect discrimination.

An AUC of 0.5 or below indicates that a diagnostic tool does not discriminate better than chance between IV+ and IV− patients, an AUC of 1 indicates a perfect discriminability.

Area under the curve (AUC) of a ROC curve indicates the ability to discriminate a true result, with values of 0.5 showing no discrimination and values of 1.0 equal to perfect discrimination.

An AUROCC value of 0.5 indicates that the classifier is completely unable to discriminate between the two classes, performing as a random predictor, while an AUROCC value of 1 indicates perfect discrimination.

Values of 0.5 indicate that the risk predictions are no better than a coin toss at discriminating a high-risk from a low-risk individual, and values of 1.0 show that the risk prediction can make a perfect discrimination.