A good ROC chart has a very steep initial slope and levels off quickly. That is, for
each misclassification of an observation, significantly more observations are correctly
classified. For a perfect model, one
with no false positives and no false negatives, the ROC chart would start at (0,0),
continue vertically to (0,1), and then horizontally to (1,1). In this instance, the
model would correctly classify every observation before a single misclassification
could occur.
The ROC chart includes two lines to help you interpret the ROC chart. The first line
is a
baseline model that has a slope of 1. This line mimics a model that correctly classifies observations
at the same rate it incorrectly classifies them. An ideal ROC chart maximizes the
distance between the
baseline model and the ROC chart. A model that classifies more observations incorrectly than
correctly would fall below the baseline model. The second line is a vertical line
at the false positive rate where the difference between the Kolmogorov-Smirnov values
for the ROC chart and
baseline models is maximized.