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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.