# Deciding between precision and recall
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Each `FindMatches` transform contains a `precision-recall` parameter. You use this parameter to specify one of the following:
+ If you are more concerned about the transform falsely reporting that two records match when they actually don't match, then you should emphasize *precision*. 
+ If you are more concerned about the transform failing to detect records that really do match, then you should emphasize *recall*.

You can make this trade-off on the AWS Glue console or by using the AWS Glue machine learning API operations.

**When to favor precision**  
Favor precision if you are more concerned about the risk that `FindMatches` results in a pair of records matching when they don't actually match. To favor precision, choose a *higher* precision-recall trade-off value. With a higher value, the `FindMatches` transform requires more evidence to decide that a pair of records should be matched. The transform is tuned to bias toward saying that records do not match.

For example, suppose that you're using `FindMatches` to detect duplicate items in a video catalog, and you provide a higher precision-recall value to the transform. If your transform incorrectly detects that *Star Wars: A New Hope* is the same as *Star Wars: The Empire Strikes Back*, a customer who wants *A New Hope* might be shown *The Empire Strikes Back*. This would be a poor customer experience. 

However, if the transform fails to detect that *Star Wars: A New Hope* and *Star Wars: Episode IV—A New Hope* are the same item, the customer might be confused at first but might eventually recognize them as the same. It would be a mistake, but not as bad as the previous scenario.

**When to favor recall**  
Favor recall if you are more concerned about the risk that the `FindMatches` transform results might fail to detect a pair of records that actually do match. To favor recall, choose a *lower* precision-recall trade-off value. With a lower value, the `FindMatches` transform requires less evidence to decide that a pair of records should be matched. The transform is tuned to bias toward saying that records do match.

For example, this might be a priority for a security organization. Suppose that you are matching customers against a list of known defrauders, and it is important to determine whether a customer is a defrauder. You are using `FindMatches` to match the defrauder list against the customer list. Every time `FindMatches` detects a match between the two lists, a human auditor is assigned to verify that the person is, in fact, a defrauder. Your organization might prefer to choose recall over precision. In other words, you would rather have the auditors manually review and reject some cases when the customer is not a defrauder than fail to identify that a customer is, in fact, on the defrauder list.

**How to favor both precision and recall**  
The best way to improve both precision and recall is to label more data. As you label more data, the overall accuracy of the `FindMatches` transform improves, thus improving both precision and recall. Nevertheless, even with the most accurate transform, there is always a gray area where you need to experiment with favoring precision or recall, or choose a value in the middle.