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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).
Poly(p-phenylene vinylene) (PPV, or polyphenylene vinylene) is a conducting polymer of the rigid-rod polymer family. PPV is the only polymer of this type that can be processed into a highly ordered crystalline thin film. PPV and its derivatives are electrically conducting upon doping.
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.
In clinical practice, post-test probabilities are often just estimated or even guessed. This is usually acceptable in the finding of a pathognomonic sign or symptom, in which case it is almost certain that the target condition is present; or in the absence of finding a sine qua non sign or symptom, in which case it is almost certain that the target condition is absent.
The simplest form of a group-contribution method is the determination of a component property by summing up the group contributions : [] = +.This simple form assumes that the property (normal boiling point in the example) is strictly linearly dependent on the number of groups, and additionally no interaction between groups and molecules are assumed.
Crossover experiments allow for experimental study of a reaction mechanism. Mechanistic studies are of interest to theoretical and experimental chemists for a variety of reasons including prediction of stereochemical outcomes, optimization of reaction conditions for rate and selectivity, and design of improved catalysts for better turnover number, robustness, etc. [6] [7] Since a mechanism ...
The more a increases the more the positive predictive value will exceed the prevalence. So far no problems. But what if a decreases? Let a = 11 (then b = 29, c = 19 and d = 41). The predictive value decreases to 27,5% and is lower than the prevalence. The more a decreases the more the positive predictive value will decrease.