What is Precision and Recall?
Two metrics for measuring AI accuracy. Precision measures how many selected items are relevant. Recall measures how many relevant items were selected.
Why It Matters
Understanding precision and recall helps you tune AI systems for your specific needs, like catching all spam vs avoiding false positives.
Real-World Example
A spam filter with high precision rarely marks good emails as spam, while high recall catches most actual spam.
“Understanding terms like Precision and Recall matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
F1 Score
A single number that balances precision and recall into one measure of model accuracy.
Confusion Matrix
A table that shows how often a classification model gets things right and wrong for each category.
Model Evaluation
The process of measuring how well an AI model performs on tasks it was designed for.
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Related Terms
Model Evaluation
The process of measuring how well an AI model performs on tasks it was designed for.
F1 Score
A single number that balances precision and recall into one measure of model accuracy.
Confusion Matrix
A table that shows how often a classification model gets things right and wrong for each category.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.