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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does accuracy measure in a classification model?
💡 Hint: Also recall that TP + TN contributes to this measure.
Question 2
Easy
Define precision.
💡 Hint: Focus on the ratio of true positive predictions.
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 accuracy?
- (TP + TN) / Total
- (TP) / (TP + FN)
- (TP) / (TP + FP)
💡 Hint: Think about what constitutes a correct prediction.
Question 2
True or False: Recall measures the proportion of true positives out of all predicted positives.
- True
- False
💡 Hint: Revisit the definitions of precision and recall.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Your model predicts 200 spam emails, 160 are truly spam and 40 are not. Calculate accuracy, precision, recall, and F1 score.
💡 Hint: Work through each metric step-by-step using the formulas.
Question 2
You have a binary classification model with TP = 30, FP = 5, FN = 5, TN = 60. What can you say about its performance in terms of F1 Score? Is it suitable for healthcare application?
💡 Hint: Assess both precision and recall when discussing suitability.
Challenge and get performance evaluation