Selected article for: "curve area and specificity sensitivity"
Author: Lundegaard, Claus; Lund, Ole; Kesmir, Can; Brunak, Søren; Nielsen, Morten
Title: Modeling the adaptive immune system: predictions and simulations
  • Document date: 2007_12_15
    • ID: 5m269nzi_15
    Snippet: A plot of the sensitivity against the false positive rate (1-specificity) is called a receiver operating characteristic (ROC) curve (Swets, 1988) . Such a plot can be a help to set the best prediction cutoff. One of the best ways of measuring the predictive power of a method is to calculate the area under the ROC curve (AUC) since this is a threshold-independent measure. Another robust measure is the Pearson correlation coefficient (PCC), which i.....
    Document: A plot of the sensitivity against the false positive rate (1-specificity) is called a receiver operating characteristic (ROC) curve (Swets, 1988) . Such a plot can be a help to set the best prediction cutoff. One of the best ways of measuring the predictive power of a method is to calculate the area under the ROC curve (AUC) since this is a threshold-independent measure. Another robust measure is the Pearson correlation coefficient (PCC), which is a measure of how well the prediction scores correlate with the actual value on a linear scale.

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