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False Positive
Predicted
A False Positive occurs when a model incorrectly predicts a Confusion Matrix
positive outcome for a case that is actually negative. When a Yes No
model’s prediction does not match with the actual outcome. In the Yes 3 2
given example the shaded region depicts the False Positive. 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.
Some more examples of False Negative are as follows:
Predicted
• Security Systems - A facial recognition system does not identify Confusion Matrix
family members as intruders. Yes No
False Negative is an intruder is recognised as a family member. Yes 3 2
Actual
• Fire Alarm Systems - On any normal day, a fire detection No 2 3
system will not trigger fire alarm.
False Negative: The system detects a fire to detect a fire in case of no fire.
The confusion matrix with all classification outcomes based on the different values of actual and predicted labels
can be presented as follows:
Predicted
Confusion Matrix
YES NO
True Positive False Negative
YES Predicted – Yes Predicted – No
Actual - Yes Actual - Yes
Actual
False Positive True Negative
NO Predicted – Yes Predicted – No
Actual - No Actual - No
Accuracy from Confusion Matrix
Classification Accuracy is the percentage of correct predictions out of the total observations made by an AI
model. It provides a clear picture of how accurate the predictions are for the given model. A high accuracy score
generally indicates good performance, as it accounts for all correctly predicted values. The mathematical formula
for classification accuracy is:
No. of correct predictions
Classification Accuracy = × 100
Total no. of predictions
150 Artificial Intelligence Play (Ver 1.0)-X

