Practice Recall (Sensitivity) - 12.3.2 | 12. Evaluation Methodologies of AI Models | CBSE Class 12th AI (Artificial Intelligence)
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12.3.2 - Recall (Sensitivity)

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does recall measure?

💡 Hint: Think about how many of actual positives are captured.

Question 2

Easy

What is the formula for calculating recall?

💡 Hint: Recall involves true positives and false negatives.

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 recall measure in an AI model?

  • The percentage of actual positives that are correctly predicted
  • The overall accuracy of the model
  • The number of true negatives

💡 Hint: Remember, recall is all about capturing positives!

Question 2

True or False: Recall is the same as precision.

  • True
  • False

💡 Hint: Recall emphasizes capturing highs, while precision assesses correctness of predictions.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

In an AI model testing for a rare disease, there are 90 true positives, 10 false negatives, and 100 true negatives. Calculate the recall and explain its significance.

💡 Hint: Focus on true positives and false negatives for your calculation.

Question 2

Discuss how to balance recall and precision in a machine learning model designed for fraud detection in banking. What are the implications of focusing on one over the other?

💡 Hint: Reflect on the customer experience versus fraud detection efficiency.

Challenge and get performance evaluation