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The recall rate is a measure of completeness, and the precision rate is a measure of exactness.
Precision can be defined as a measure of exactness i.e. if all the predicted labels for a given class X is given, how many instances were correctly classified.
It relates to the closeness of the test results to true values, that is, measure of exactness of analytical method.
For each k, we evaluate the prediction accuracy through precision (a measure of exactness) and recall (a measure of completeness), combined into F-score.
The precision is a measure of exactness defined as the fraction of the correctly predicted voxels, (25) where # TP is the number of true positives and # TP+# FP is the total number of voxels with the same class labels.
There are two traditional accuracy measures that can provide insight into the accuracy of peak picking: the recall value or the measure of completeness, the ability to discover true peaks; and the precision value or the measure of exactness, the ability to reject false peaks.
Precision (measure of the exactness of the detection) or predictive positive value of protein detection was computed as TP/ TP+FP).
For technical reasons, NADH2 and NADPH2 as well as acetylCoA and CoA could not be measured with a high degree of exactness.
Sunlight makes of exactness an issue.
There's this element of exactness.
Kirchberg introduced weak exactness for von Neumann algebras as an analogue of exactness for C⁎-algebras.
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CEO of Professional Science Editing for Scientists @ prosciediting.com