# Machine learning measurements
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To understand the measurements that are used to tune your machine learning transform, you should be familiar with the following terminology:

**True positive (TP)**  
A match in the data that the transform correctly found, sometimes called a *hit*.

**True negative (TN)**  
A nonmatch in the data that the transform correctly rejected.

**False positive (FP)**  
A nonmatch in the data that the transform incorrectly classified as a match, sometimes called a *false alarm*.

**False negative (FN)**  
A match in the data that the transform didn't find, sometimes called a *miss*.

For more information about the terminology that is used in machine learning, see [Confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix) in Wikipedia.

To tune your machine learning transforms, you can change the value of the following measurements in the **Advanced properties** of the transform.
+ **Precision** measures how well the transform finds true positives among the total number of records that it identifies as positive (true positives and false positives). For more information, see [Precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) in Wikipedia.
+ **Recall** measures how well the transform finds true positives from the total records in the source data. For more information, see [Precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) in Wikipedia.
+ **Accuracy ** measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall. For more information, see [Accuracy and precision](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_information_systems) in Wikipedia.
+ **Cost** measures how many compute resources (and thus money) are consumed to run the transform.