Explore the options below.

Consider two models—A and B—that each evaluate the same dataset.
Which one of the following statements is true?

If Model A has better precision than model B, then
model A is better.

While better precision is good, it might be coming at the expense
of a large reduction in recall. In general, we need to look at
both precision and recall together, or summary metrics like AUC
which we'll talk about next.

If model A has better recall than model B, then model A is
better.

While better recall is good, it might be coming at the
expense of a large reduction in precision. In general, we need
to look at both precision and recall together, or summary metrics
like AUC, which we'll talk about next.

If model A has better precision and better recall than model B,
then model A is probably better.

In general, a model that outperforms another model on both
precision and recall is likely the better model. Obviously,
we'll need to make sure that comparison is being done at a
precision / recall point that is useful in practice for this
to be meaningful. For example, suppose our spam detection model
needs to have at least 90% precision to be useful and avoid
unnecessary false alarms. In this case, comparing
one model at {20% precision, 99% recall} to another at
{15% precision, 98% recall} is not particularly instructive, as
neither model meets the 90% precision requirement. But with that caveat
in mind, this is a good way to think about comparing models when using
precision and recall.