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 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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