It’s important to understand these definitions as they form a key part of explaining other fundamental Machine Learning concepts like __precision__ and __recall__.

The definitions below are outlined in the context of their application within the Re:infer platform.

**To start with:**

- A ‘positive’ prediction is one where the model thinks that a label applies to a verbatim
- A ‘negative’ prediction is one where the model thinks that a label does not apply to a verbatim

**True positives**

A *true positive* result is one where the model __correctly__ predicts that a label applies to a verbatim.

**True negatives**

A *true negative* result is one where the model __correctly__ predicts that a label does not apply to a verbatim.

**False positives**

A *false positive *result is one where the model __incorrectly__ predicts that a label applies to a verbatim, when in fact it does not apply.

**False negatives**

A *false negative *result is one where the model __incorrectly__ predicts that a label does not apply to a verbatim, when in fact it does apply.

To understand each of these concepts in more detail, please see __here__.

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