why does scikitlearn says F1 score is ill-defined with FN bigger than 0?

Question

I run a python program that calls sklearn.metrics\'s methods to calculate precision and F1 score. Here is the output when there is no predicted sample:

Answer 1

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/classification.py

F1 = 2 * (precision * recall) / (precision + recall)

precision = TP/(TP+FP) as you've just said if predictor doesn't predicts positive class at all - precision is 0.

recall = TP/(TP+FN), in case if predictor doesn't predict positive class - TP is 0 - recall is 0.

So now you are dividing 0/0.