Precision
The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive.
Related Concepts
- Recall: Explore how Recall relates to Precision
- F1 Score: Explore how F1 Score relates to Precision
- Confusion Matrix: Explore how Confusion Matrix relates to Precision
- True Positive: Explore how True Positive relates to Precision
Why It Matters
Understanding Precision is crucial for anyone working with model evaluation. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Confusion Matrix
A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.
F1 Score
The harmonic mean of precision and recall, providing a single metric that balances both concerns.
Recall
The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.
True Positive
Correctly predicted positive cases in classification.