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Some more examples of True Negative are as follows:
• Spam Detection - The model predicts an email is “Not Spam”.
True Negative is when an email is indeed not spam.
• Loan Default Prediction - The model predicts a customer will not default, in payment of loan instalment on time.
True Negative is the customer paid and did not default.
False Positive
A False Positive occurs when a model incorrectly predicts a positive outcome for a case that is actually negative.
When a model’s prediction does not match with the actual outcome. In the given example the shaded region
depicts the False Positive.
Predicted
Confusion Matrix
Yes No
Yes 3 2
Actual
No 2 3
Some more examples of False Positive are as follows:
• Hiring Systems (AI-based Recruitment) - An AI system screens suitable job applicants.
False Positive is a less-qualified candidate is identified as a good match.
• Autonomous Vehicles (Object Detection) - A self-driving car detects obstacles on the road.
False Positive is the car incorrectly identifies a harmless shadow as an obstacle.
False Negative
A False Negative occurs when a model incorrectly predicts a negative outcome when the true outcome is actually
positive. When a model’s prediction does not match with the actual outcome. In the given example the shaded
region depicts the False Negative.
Predicted
Confusion Matrix
Yes No
Yes 3 2
Actual
No 2 3
Some more examples of False Negative are as follows:
• Security Systems - A facial recognition system does not identify family members as intruders.
False Negative is an intruder is recognised as a family member.
• Fire Alarm Systems - On any normal day, a fire detection system will not trigger fire alarm.
False Negative: The system detects a fire to detect a fire in case of no fire.
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