Practice Recall (Sensitivity) - 8.5 | Chapter 8: Model Evaluation Metrics | Machine Learning Basics
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8.5 - 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?

πŸ’‘ Hint: Think about what happens in terms of True Positives.

Question 2

Easy

What is the formula for recall?

πŸ’‘ Hint: Recall involves True Positives and places them in relation with 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 specifically measure in a model evaluation?

  • The percentage of positive predictions
  • The percentage of actual positives correctly predicted
  • The overall accuracy of the model

πŸ’‘ Hint: Think of how well the model detects true positives.

Question 2

True or False: Recall can be negatively impacted in imbalanced datasets.

  • True
  • False

πŸ’‘ Hint: Consider how classes are represented in the dataset.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

A model identifies 100 true positives out of 150 actual positives, while also mistakenly labeling 20 negatives as positives. Calculate recall, and discuss why it might be crucial to improve this number in a healthcare setting.

πŸ’‘ Hint: Use the recall formula and think about its implications in healthcare.

Question 2

Discuss the trade-offs between precision and recall in fraud detection. How might a focus on improving recall impact precision?

πŸ’‘ Hint: Reflect on the balance these metrics represent and the context of their application.

Challenge and get performance evaluation