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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does the term True Positive (TP) refer to?
💡 Hint: Think about when the model performs effectively in identifying positive cases.
Question 2
Easy
How would you define accuracy?
💡 Hint: It's a measure of overall effectiveness.
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 is the formula for calculating accuracy?
- (TP + TN) / (TP + TN + FP + FN)
- (TP) / (TP + FP)
- (TP) / (TP + FN)
💡 Hint: Remember all components included in the accuracy calculation.
Question 2
True or False: Recall is also known as sensitivity.
- True
- False
💡 Hint: Think about terms used interchangeably in the context of model evaluation.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
An AI model predicts loan approvals with the following results: 100 actual approvals, 80 correct predictions, 20 incorrect predictions due to false negatives. Calculate the recall.
💡 Hint: Reference the definition of recall and ensure you identify TP and FN accurately.
Question 2
A model exhibits high precision but low recall. Discuss potential ramifications of this imbalance.
💡 Hint: Think about the implications of prioritizing one metric over another in practical applications.
Challenge and get performance evaluation