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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define specificity in the context of AI models.
💡 Hint: Think about what it means for negative cases.
Question 2
Easy
What does TN stand for?
💡 Hint: Remember it's about correct predictions for negative cases.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does specificity measure in model evaluation?
- Correct predictions of positives
- Correct predictions of negatives
- Overall accuracy
💡 Hint: Think about what it means for negative cases.
Question 2
True or False: A high specificity ensures that negative cases are often misclassified as positive.
- True
- False
💡 Hint: Consider how false positives affect specificity.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
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?
💡 Hint: Use the definition of specificity to adjust your calculations.
Question 2
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?
💡 Hint: Model the logic of the specificity formula here.
Challenge and get performance evaluation