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The curve is created by plotting the true positive rate against the false positive rate as shown in Fig. 9.
ROC is drawn by plotting the true alarm rate against the false alarm rate at various settings.
The ROC curve is generated by plotting the true positive rate against the false positive rate while changing the cutoff from the highest to lowest prediction score.
To test the performance of this method at various score thresholds, we created an ROC curve (Receiver and Operating Characteristics) by plotting the true positive rate against the false positive rate (Figure 4).
The ROC curve is obtained by plotting the true positive rate as a function of the false positive rate or, equivalently, sensitivity versus (1-specificity) as the discrimination threshold of the binary classifier is varied.
ROC curves are created by plotting the true positive rate (sensitivity) against false positive rate (1-specificity) obtained at each possible threshold value.
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The optimal cutoff values of waist circumference were calculated by plotting the true-positive rate (sensitivity) against the false-positive rate (1-specificity).
A receiver operating characteristic curve was obtained by plotting the true-positive proportion (sensitivity) against the false-negative proportion (1 – specificity).
Receiver operator characteristic (ROC) curves were constructed by plotting the true-positive rate (sensitivity) against the false-positive rate (1−specificity) for each miRNA and for the best combination of miRNAs.
An ROC curve is constructed by plotting the true-positive rate against the false-negative rate (i.e. one minus true-negative rate) over a range of cut-off scores (Fletcher and Fletcher 2005).
The logistic regression formula was as follows: A receiver-operating characteristic curve was generated by plotting the true-positive rate (sensitivity) on the y axis and the false-positive rate (1−specificity) on the x axis (Tanaka et al, 1999).
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by plotting the relative
by smoothing the true
by plotting the cumulative
by plotting the average
by plotting the logarithmic
by plotting the freezing
by recovering the true
by plotting the experimental
by plotting the aggregated
by plotting the -1nL
by plotting the mean
by dividing the true
by understanding the true
by examining the true
by estimating the true
by plotting the Ct
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