Understanding ROC curve

Hi all,

I know the basics of machine learning and the logic behind many algorithms. But no reading helped intuitively understand the AUC metric used for a kaggle competition. I wanted to understand intuitively ROC curve and what increases area under curve. So I built a simple graphic using d3 at http://www.navan.name/roc/. You can drag the threshold bar and also adjust position of the curve for negatives using the slider at the top of graphic. You can play with these values to understand its effect on the ROC curve. It is not very polished, but it served the purpose for me.

I posted it to kaggle forums initially and I am posting it here in the hope it benefits others who are in the same boat.