Visualize the performance of the classification model fit (prediction of the gene associated peaks).
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Further arguments passed to plot.
plotROC is a simple wrapper for the
The ROC curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings. The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. The area under the curve (AUC) is a measure of accuracy.
Armen R. Karapetyan
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