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What does a high AUC but low F1 score mean?
high AUC ROC vs low f1 or other “point” metric, means that your classifier currently does a bad job, however you can find the threshold for which its score is actually pretty decent.
Why are F1 scores so low?
An F1 score reaches its best value at 1 and worst value at 0. A low F1 score is an indication of both poor precision and poor recall. The machine-learning annotator generates erroneous annotations and fails to find annotations that it should have found.
What is the definition of F1 score and how do you interpret a high F1 score?
F1 score – F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution.
What’s the difference between F1 and ROC AUC?
F1 score is applicable for any particular point on the ROC curve. You may think of it as a measure of precision and recall at a particular threshold value whereas AUC is the area under the ROC curve. For F score to be high, both precision and recall should be high.
What does it mean to have high AUC but low F1 score?
F1 uses a specific cut off point which should be chosen wisely. Also for highly imbalanced data it can be very difficult to get high recall on the rare class with reasonable precision. AUC for better or worse is indifferent to class imbalance.
Which is better TPR or ROC AUC score?
Of course, the higher TPR and the lower FPR is for each threshold the better and so classifiers that have curves that are more top-left-side are better. An extensive discussion of ROC Curve and ROC AUC score can be found in this article by Tom Fawcett.
What’s the difference between F1 and F scores?
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.