📚 node [[precision|precision]]
Welcome! Nobody has contributed anything to 'precision|precision' yet. You can:
-
Write something in the document below!
- There is at least one public document in every node in the Agora. Whatever you write in it will be integrated and made available for the next visitor to read and edit.
- Write to the Agora from social media.
-
Sign up as a full Agora user.
- As a full user you will be able to contribute your personal notes and resources directly to this knowledge commons. Some setup required :)
⥅ related node [[precision_target]]
⥅ related node [[average_precision]]
⥅ related node [[precision recall_curve]]
⥅ related node [[precision]]
⥅ related node [[precision fermentation]]
⥅ node [[precision]] pulled by Agora
📓
garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Precision.md by @KGBicheno
precision
Go back to the [[AI Glossary]]
A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class. That is:
$$ \text Precision = \frac{True Positives}{True Positives + False Positives} $$
⥅ node [[precision-fermentation]] pulled by Agora
precision fermentation
where proteins and fats are produced in breweries
⥅ node [[precision-recall_curve]] pulled by Agora
📓
garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Precision-Recall_Curve.md by @KGBicheno
precision-recall curve
Go back to the [[AI Glossary]]
A curve of precision vs. recall at different classification thresholds.
⥅ node [[precision_target]] pulled by Agora
📖 stoas
- public document at doc.anagora.org/precision|precision
- video call at meet.jit.si/precision|precision
🔎 full text search for 'precision|precision'