I can understand why you might want that to be so, but to me it's a bigger problem that in a multilabel problem with these true values:

```[
[],
[1]
]
```
you get 0 recall and 0 precision no matter your prediction, when surely `[]` is as precise a prediction as you can make! In the second case, if you predict 1 as well as infinitely many wrong values, i.e. `y_pred[1] = [1, 2, 3, 4, ...]`, you still get recall = 1, while precision -> 0!

This applies too to label-based averaging, although there such cases are considered pathological.