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The Equations (1) and (2) assume the performance of predictions is constant, modeled by parameters P T and P F; however, this is not true in practice.
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A homogeneous ROC curve assumes the performance of the test (as measured by LOR) is the same across the whole range of specificity.
"It largely has to do with a desire for greater accountability," Mr. Beals said, adding that "given tougher economic times, I can only assume the use of performance incentives is only going to increase in coming years".
The algorithm to model and predict a classifier's performance contains three steps: 1) Learning curve creation; 2) Model fitting; 3) Sample size prediction; Assuming the target performance measure is classification, a learning curve that characterizes classification accuracy (Yacc), as a function of the training set size (X) is created.
To model this uncertainty, we assume that the performance of a system for providing a capability has lower and upper bounds and subject to complete uncertainty, i.e., no information is available about the probability distribution of the performance values.
On the other hand, we may assume that the performance of the classifier could not be guaranteed in the case of images that are very much dissimilar from the elements of the training set.
Whilst it is untenable to assume that the performance is uniform along the whole spectrum of severity, it is even less likely it changes many times.
We assume that the performance of each algorithm is consistent for all miRNAs.
They might assume that the performance of clustering can be measured indirectly by the performance of abbreviation disambiguation.
LTM assume that the performance of an individual while answering the items is explained by one or more (continuous) variables, commonly called "latent variables".
We assume that the performance, in general, as well as in comparison to linear mixed models should essentially be the same.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com