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Having two plots of ROC and PR curves (by scikit-learn) on one dataset raised me a question.

The generated Precision-recall plot shows high precision and high recall, that is, low false positive rate and low false negative rate, and the ROC curve shows that when the false positive rate is low, the true positive rate is low too. Images below.

Note: the given dataset is highly imbalanced: one class has 4 text objects, the other has hundreds.

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I am having a hard time understanding what this could mean. Any help in this interpretation is deeply appreciated. Thanks

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The dataset is imbalanced. ROC is not sensitive to the number of examples ratios whereas Precision Recall is highly sensitive. Check this link https://classeval.wordpress.com/simulation-analysis/roc-and-precision-recall-with-imbalanced-datasets/ it has very good plots.

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