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This ROC graph gives a good pictorial representation of the recall-precision tradeoff.
This ROC plot also shows robust performance of the alternating decision tree classifier compared with other similar studies which uses Bayesian network and maximum likelihood estimation.
This ROC curve cannot be expressed by one equation by eliminating the detector threshold, as in (19), due to the complexity of the equations.
This ROC curve is shown in Figure 1C.
This ROC curve, shown in Figure 11 appears very close to the random classifier line, as would be expected.
This ROC area indicates low correlation between the trees of genes annotated as being involved in this process.
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Based on this ROC-defined cut-off level, the sensitivity and specificity of DCP were 96.8 and 98.3 % respectively.
In this paper, the ROC skeleton approach is proposed for efficiently estimating the operating characteristic.
In this figure, the ROC using linear discriminant analysis (LDA) and GMM classifiers are displayed.
Based on this idea, the ROC analysis [32, 33] can be introduced to quantify detection accuracy.
In this work, the ROC curve is used to select the correspondence threshold CT that would provide an optimum trade-off between the TPrate and the FPrate rate of edge detectors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com