F1-score is the harmonic mean of precision and recall. The y-axis of recall is true positive rate (which is also recall). So, sometime classifiers can have low recall but very high AUC, what that means?
What are the differences between AUC and F1-score?
F1 score is applicable for any particular point of the ROC curve. This point may represent for example a particular threshold value in a binary classifier and thus corresponds to a particular value of precision and recall.
Remember, F score is a smart way to represent both recall and precision. For F score to be high, both precision and recall should be high.
Thus, the ROC curve is for various different levels of thresholds and has many F score values for various points on its curve.