Model Metrics Mastery Quiz: Evaluating Machine Learning Performance — Questions & Answers

This quiz contains 10 questions. Below is a complete reference of all questions, answer choices, and correct answers. You can use this section to review after taking the interactive quiz above.

1. Question 1: Understanding Accuracy

   In a binary classification problem, what does the accuracy metric measure?

   • The proportion of correct predictions to total predictions
   • The percentage of false positives among all positive predictions
   • The ratio of true negatives to false negatives
   • The number of true positives divided by total samples
   • The sum of recall and precision
   Show correct answer

   Correct answer: The proportion of correct predictions to total predictions

2. Question 2: Precision vs. Recall

   If a medical test for a rare disease has high recall but low precision, what does this indicate about the test?

   • It often correctly detects most actual diseases cases, but also gives many false alarms
   • It rarely detects the disease but is always correct when it does
   • It never produces false positives
   • It has more true negatives than true positives
   • It always has a higher F1-score than accuracy
   Show correct answer

   Correct answer: It often correctly detects most actual diseases cases, but also gives many false alarms

3. Question 3: True Negatives in Confusion Matrix

   Which part of the confusion matrix corresponds to true negatives?

   • The lower-right cell
   • The bottom-left cell
   • The top-right cell
   • The lower-middle cell
   • The upper-left cell
   Show correct answer

   Correct answer: The upper-left cell

4. Question 4: Evaluating F1-Score

   Why might you prefer the F1-score over accuracy when evaluating a model on an imbalanced dataset?

   • F1-score balances both precision and recall, whereas accuracy can be misleading if classes are imbalanced
   • F1-score is only useful when there are no true negatives
   • Accuracy penalizes false positives more than false negatives
   • F1-score is easier to interpret than recall
   • F1-score is always higher than accuracy
   Show correct answer

   Correct answer: F1-score balances both precision and recall, whereas accuracy can be misleading if classes are imbalanced

5. Question 5: Interpreting ROC-AUC

   What does a ROC-AUC score of 0.5 indicate about a classifier’s performance?

   • The classifier performs no better than random chance
   • The classifier is perfect
   • The classifier has high precision but low recall
   • The classifier predicts all classes correctly
   • The classifier has maximized the F1-score
   Show correct answer

   Correct answer: The classifier performs no better than random chance

6. Question 6: Precision Calculation

   Given 80 true positives, 20 false positives, and 100 false negatives, what is the precision?

   • 0.80
   • 0.44
   • 0.20
   • 0.67
   • 0.50
   Show correct answer

   Correct answer: 0.80

7. Question 7: Model Selection with Metrics

   You have two models: Model A with higher accuracy but lower recall, and Model B with slightly lower accuracy but much higher recall. When would Model B be preferred?

   • When missing positive cases is more costly than having false alarms
   • If true negatives are the most important
   • When the classes are perfectly balanced
   • If precision is the only metric to optimize
   • When Model A has a better ROC-AUC
   Show correct answer

   Correct answer: When missing positive cases is more costly than having false alarms

8. Question 8: Averaging in Multi-class Problems

   Which method of averaging precision, recall, and F1-score treats all samples equally regardless of class size?

   • Macro-average
   • Micro-average
   • Weighted-average
   • Harmonic mean
   • Geometric mean
   Show correct answer

   Correct answer: Micro-average

9. Question 9: Metric Implementation in Code

   Which of the following scikit-learn functions can you use to compute the F1-score for a binary classification problem in Python?

   • f1_score(y_true, y_pred)
   • accuracy(y_true, y_pred)
   • roc_auc_score(y_pred, y_true)
   • calculate_f1(y_pred, y_true)
   • recall_score(y_true, y_pred)
   Show correct answer

   Correct answer: f1_score(y_true, y_pred)

10. Question 10: Ready for Production?

    A model deployed to production must meet certain metric thresholds on unseen data. Why is relying solely on training metrics a bad idea?

    • Training metrics can be overly optimistic and may not reflect true performance on unseen data
    • Training metrics are always lower than test metrics
    • Test metrics are ignored by most organizations
    • Unseen data has no impact on production decisions
    • Training metrics measure only recall
    Show correct answer

    Correct answer: Training metrics can be overly optimistic and may not reflect true performance on unseen data