C-Statistic and ROC Curves

The C-Statistic, also called Concordance Statistic or C-Stat, is a common metric used analyze performance of binary classification models, and to compare multiple models to one another.
Specifically, the C-statistic is computed as the area under the ROC curve. The minimum value of c is 0.0 and the maximum is 1.0. C-values of 0.7 to 0.8 to show acceptable discrimination, values of 0.8 to 0.9 to indicate excellent discrimination, and values of ≥0.9 to show outstanding discrimination.
An ROC curve is a graphical plot showing the true positive rate (TPR) against the false positive rate (FPR). TPR is also known as Sensitivity while FPR can be calculated as one minus the Specificity.  The C-Statistic will be a larger value, near 1.0, when the ROC curve hugs the upper left side of the plot.  This represents the situation of model that has a good mix of low false positive rate and high true positive rate.  A model that is not much better than just randomly assigning outputs will have an ROC curve near the 45 degree line, and a C-Statistic near 0.5.
For more information on ROC Curves and the C-Statistic, see: