A curve showing the tradeoff between precision and recall at different thresholds.
This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.
Related Concepts
- Precision
- Recall
- Evaluation
Standard 6 stops to get here · leads to 1
Precision-Recall Curve
A curve showing the tradeoff between precision and recall at different thresholds.
Your route here
6 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Classification ✓ understood
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
- Confusion Matrix ✓ understood
A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.
- Precision ✓ understood
The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive.
- Recall ✓ understood
The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.
- Precision-Recall Curve · you are here ✓ understood
Where it sits
Explore nearby
Evaluation ROC Curve Receiver Operating Characteristic curve - plots true positive rate vs false positive rate at various classification thresholds. Evaluation Average Precision The weighted mean of precisions at each threshold, where the weight is the increase in recall from the previous threshold. Evaluation F1 Score The harmonic mean of precision and recall, providing a single metric that balances both concerns. Evaluation AUC Area Under the Curve - measures the area under the ROC curve, indicating classification model performance (1.0 is perfect).