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The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
Predictive value of tests is the probability of a target condition given by the result of a test, [1] often in regard to medical tests.. In cases where binary classification can be applied to the test results, such yes versus no, test target (such as a substance, symptom or sign) being present versus absent, or either a positive or negative test), then each of the two outcomes has a separate ...
The individual's pre-test probability was more than twice the one of the population sample, although the individual's post-test probability was less than twice the one of the population sample (which is estimated by the positive predictive value of the test of 10%), opposite to what would result by a less accurate method of simply multiplying ...
In information retrieval, the positive predictive value is called precision, and sensitivity is called recall. Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents.
Any definition of expected value may be extended to define an expected value of a multidimensional random variable, i.e. a random vector X. It is defined component by component, as E[ X ] i = E[ X i ] .
Precision and recall. In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly ...
Predicted outcome value theory is an alternative to uncertainty reduction theory, which Charles R. Berger and Richard J. Calabrese introduced in 1975. Uncertainty reduction theory states that the driving force in initial interactions is to collect information to predict attitudes and behaviors for future relationship development.