Different score range when calculating area of under curve in ROC curves I have two classifiers which try to classify the same data sets. In order to check the efficiency of the classifiers I intend to plot the curves and calculate the AUC value. The concern is that one of the classifiers always produce positive values as the scores (actually it is the p value) but the other classifier produces both positive and negative values as the scores(it has its own producing score method rather than producing pvalues). Should I take the absolute values for the second scores when plotting the roc curves and calculating the AUC or I can use scores as they are (both positive and negative). When I make the absolute values for the second classifier to plot the curves the AUC amount decreases. 
Thank you in advance.
 A: Always use the scores the way they come out of the classifier. If you take absolute values you are basically changing the classifier's ranking and you will obtain an erroneous ROC curve.
The values of scores across classifiers are entirely irrelevant: the only thing that matters to plot ROC curves is the ranking produced by each classifier, which is based on the scores. In ROC analysis you only compare rankings, irrelevant of what the scores may have been.
If you insist on transforming scores (which is useless), whatever transformation you do must keep the original ranking intact. For example, you could scale the scores with a positive constant or add an arbitrary (but constant!) value to them.
A: A classifier often involves an internal (hidden) value which is compared against a threshold.  Effectively in a ROC curve each such value is treated as a threshold to get each new segment of the curve.
If you are worried about the different range of values, and their unintepretability, it is nice to convert them to a probability.  You can do that by reading off new numbers from the ROC curve as each point corresponds to a particular number of positives and negatives that are above threshold. In fact you can derive both a cumulative probability and an incremental probability that relates to the slope at a returned value (versus the slope of the chance line).
There are a couple of papers around that go into detail about this...
