How to interpret the AUC score in this case?

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I just run a random forest model on a imbalance dataset. I got the set of AUC and the confusion matrix. The AUC seemed not bad but actually the model predict every instance as positive. So how it happened and how to use AUC properly?

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The ROC Curve as below:

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pythonmachine-learningrandom-forestconfusion-matrixauc
Cross-posted: stackoverflow.com/q/41132399/781723, datascience.stackexchange.com/q/15725/8560. Please do not post the same question on multiple sites. Each community should have an honest shot at answering without anybody's time being wasted. - D.W.

1 Answers

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You can have this problem when your data is skewed in one direction or the other (sort of similar to a small false positive rate being terrible for medical tests for rare conditions). It might be helpful to look at the entire receiver operating characteristic curve (ROC curve) instead of just the AUC summary score.