Clasification - ROC Curve with very high number of false negatives I have the following curve as the result of a azure machine learning classification experiment.

This obviously shows a lot of false negatives, but I'm struggling to interpret this, as my data has a high number of negatives and a low number of positives (I'm doing a binary classification).
I am interpreting this as saying my data is almost entirely made up of positive values, with the majority being identified as negative. I believe that the false negatives on the chart are actually true negatives. Am I reading the chart correctly?
Notice how the precision is very high and all of the other metrics are very low.  Now look at the class balance of your problem:

$$TP+FN=Actual Positive=31,245$$
$$TN+FP=Actual Negative=508$$

So your data is heavily skewed toward positives.  To have gotten a model that is producing this poorly, I think you may have provided the model with the precision as the cross validation metric.  The precision is a very bad cross validation metric in this case since it will result in poor accuracy and poor recall.  accuracy is also not a good metric as your model could classify everything as positive and get an accuracy of:

$$AC=\frac{31,245}{31,763}=.984$$

For cases like this where the classes are grossly weighted toward one value, I suggest using the F1-score as your cross validation metric.  The F1-score is the harmonic mean of precision and recall and hence balances these two factors nicely.  Wikipedia actually has a very nice explanation of classification metrics here and this paper is top notch if you even need to understand multi-class metrics and confusion matrices.

Hope this helps!