Above answers are both good. 

But what I want to point out is AUC (Area under ROC) is problematic especially the data is imbalanced (so called highly skewed: $Skew=\frac{negative\;examples}{positive\;examples}$ is large). This kind of situations is very common in action detection, fraud detection, bankruptcy prediction ect. That is, the positive examples you care have relatively low rates of occurrence. 

With imbalanced data, the AUC still gives you suspicious value around 0.8. However, it is high due to large TN, rather than the large TP (True positive).

Such as the example below, 

    TP=155,   FN=182
    FP=84049, TN=34088


So when you use AUC to measure the performance of classifier, the problem is the increasing of AUC doesn't really reflect a better classifier. It's just the side-effect of too many negative examples. You can simply try in you imbalanced dataset, you will see this issue. 

The paper [Facing Imbalanced Data Recommendations for the Use of Performance Metrics][1] found "while ROC was unaffected by skew, the precision-recall curves suggest that ROC may mask poor performance in some cases." Searching for a good performance metrics is still a open question. A general F1-score may help $$
F_\beta = (1 + \beta^2) \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}}{(\beta^2 \cdot \mathrm{precision}) + \mathrm{recall}}$$

where the $\beta$ is the relative importance of precision comparing to recall. 

Then, my suggestions for imbalanced data are similar to [this post][2]. You can also try the decile table, which can be construct by searching "Two-by-Two Classification and Decile Tables". Meanwhile, I am also studying on this problem and will give better measure. 


  [1]: http://ieeexplore.ieee.org/document/6681438/?arnumber=6681438&tag=1
  [2]: http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/