true positive
What It Means
A true positive occurs when your AI system correctly identifies something as belonging to the category you're looking for. It's like a security camera that successfully spots an actual intruder - the system said 'threat detected' and there really was a threat.
Why Chief AI Officers Care
True positives represent your AI system working as intended, directly contributing to business value and ROI. High true positive rates indicate your AI is successfully catching the cases that matter most - whether that's identifying qualified leads, detecting fraud, or flagging quality issues before they reach customers.
Real-World Example
A bank's fraud detection system flags a transaction as suspicious, and upon investigation, it turns out to be actual fraud that gets blocked, saving the bank and customer from financial loss. The system correctly identified a real threat.
Common Confusion
People often think having lots of true positives automatically means the system is performing well, but you need to consider false positives too - a system that flags everything as positive will catch all the real cases but create massive operational overhead.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, a true positive occurs when your diagnostic or screening system correctly identifies a patient who act...
Finance: In finance, a true positive occurs when an AI model correctly identifies actual instances of the target condition, such ...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"an outcome where the model correctly predicts the positive class. "Source: google_dev_classification-true-false-positive-negative
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