The phrase "a positive instance" is correct and usable in written English.
It can be used in contexts where you want to refer to a specific example that demonstrates a desired or favorable outcome.
Example: "In our study, we found a positive instance of the treatment's effectiveness in reducing symptoms."
Alternatives: "a favorable example" or "a beneficial case".
Celebrities can be pretty influential, like in Scientology or something, so this is a positive instance: they are encouraging people to come to a festival.
But nothing can be a positive instance of the latter, and hence (Aa ∧ Ba) cannot support it.
For instance, Crispin Wright and Stewart Shapiro say a competent speaker can faultlessly classify the borderline case as a positive instance while another competent speaker can faultlessly classify the case as a negative instance.
Each relation instance was searched for in the SpanishDrugEffectDB database in order to know if it is a positive instance.
7, 13 We represent the subsequence that has S or T residue at the center and experimentally verified to be glycosylated as a positive instance.
One training instance was created as a positive instance from every coreferent event pair in which a temporal relation exists, labeling it with the corresponding relation type.
False positive is misclassifying an actual negative instance as a positive; false negative is misclassifying an actual positive instance as a negative; and true positive and true negative are the correct classifications.
The AUC is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one, whereas a receiver operating characteristic (ROC) plot provides a graphical evaluation of true-positives (sensitivity) versus false-positives (1-specificity) tradeoffs.
The AUC can be interpreted as the probability that a classifier ranks a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative'negative
The area under the ROC curve (AUC) is the quantitative measure of the performance of a classifier and is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative example.
We also report the ROC AUC, the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one (Fawcett, 2006).
