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  2. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    To calculate the recall for a given class, we divide the number of true positives by the prevalence of this class (number of times that the class occurs in the data sample). The class-wise precision and recall values can then be combined into an overall multi-class evaluation score, e.g., using the macro F1 metric. [21]

  3. Evaluation measures (information retrieval) - Wikipedia

    en.wikipedia.org/wiki/Evaluation_measures...

    An alternative is to derive an analytical () function by assuming a particular parametric distribution for the underlying decision values. For example, a binormal precision-recall curve can be obtained by assuming decision values in both classes to follow a Gaussian distribution. [12]

  4. SDTM - Wikipedia

    en.wikipedia.org/wiki/SDTM

    Data stored in dataset variables include both raw (as originally collected) and derived values (e.g., converted into standard units, or computed on the basis of multiple values, such as an average). In SDTM only the name, label, and type are listed with a set of CDISC guidelines that provide a general description for each variable used by a ...

  5. F-score - Wikipedia

    en.wikipedia.org/wiki/F-score

    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 ...

  6. Classification Tree Method - Wikipedia

    en.wikipedia.org/wiki/Classification_Tree_Method

    Each classification can have any number of disjoint classes, describing the occurrence of the parameter. The selection of classes typically follows the principle of equivalence partitioning for abstract test cases and boundary-value analysis for concrete test cases. [5] Together, all classifications form the classification tree.

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  8. Testing effect - Wikipedia

    en.wikipedia.org/wiki/Testing_effect

    The test format doesn't seem to impact the results as it is the process of retrieval that aids the learning [79] but transfer-appropriate processing suggests that if the encoding of information is through a format similar to the retrieval format then the test results are likely to be higher, with a mismatch causing lower results. [80]

  9. US FDA classifies corrections to instructions for Abiomed's ...

    www.aol.com/news/us-fda-classifies-recall...

    Abiomed's so-called recall of some models of its Impella Left Sided Blood Pumps, used in some high-risk heart procedures to provide short-term support, was because of the possibility they could ...

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