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2 cards from this section. Quick, good the night before a test.
Define specificity in the context of AI models.
Think about what it means for negative cases.
What does TN stand for?
Remember it's about correct predictions for negative cases.
What does specificity measure in model evaluation?
Think about what it means for negative cases.
True or False: A high specificity ensures that negative cases are often misclassified as positive.
Consider how false positives affect specificity.
An AI model identifies users as either legitimate (positive) or impostors (negative). If the model has a specificity of 0.95 and observes 100 actual impostors, how many are likely to be incorrectly categorized as legitimate?
Use the definition of specificity to adjust your calculations.
In a healthcare application, a test shows a specificity of 0.9. If 200 tests result in false positives, how would you leverage this to assess the performance of the test?
Model the logic of the specificity formula here.
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