Classification Metrics Quiz
Test your knowledge in the machine learning classification metrics with an objective-type quiz
Keywords
machine learning, machine learning classification, machine learning classification metrics, decision trees, python, precision, recall, f1 score, weighted, accuracy, linear regression
1. What does the accuracy metric measure in a classification model?
- A. The ratio of correctly predicted instances to the total instances.
- B. The proportion of positive cases that were correctly identified.
- C. The proportion of actual positives that were correctly identified.
- D. The proportion of predicted positive cases that were correct.
Reveal answer
A. The ratio of correctly predicted instances to the total instances.
2. Which metric is most suitable for evaluating the performance of a classification model when dealing with imbalanced datasets?
- A. Accuracy
- B. Precision
- C. Recall
- D. F1-Score
Reveal answer
D. F1-Score
3.What does precision measure in the context of classification?
- A. The ratio of true positives to the sum of true positives and false positives.
- B. The ratio of true positives to the sum of true positives and false negatives.
- C. The ratio of correctly predicted instances to the total instances.
- D. The ratio of false negatives to the sum of false negatives and true positives.
Reveal answer
D. F1-Score
4. What does recall (or sensitivity) measure?
- A. The ratio of true positives to the sum of true positives and false positives.
- B. The ratio of true positives to the sum of true positives and false negatives.
- C. The ratio of true negatives to the sum of true negatives and false positives.
- D. The ratio of false negatives to the sum of false negatives and true positives.
Reveal answer
B. The ratio of true positives to the sum of true positives and false positives.
5. The F1-score is the harmonic mean of which two metrics?
- A. Accuracy and Precision
- B. Precision and Recall
- C. Recall and Specificity
- D. Specificity and Precision
Reveal answer
B. Precision and Recall
6. In a confusion matrix, what does the bottom-right cell represent?**
- A. True Positives (TP)
- B. True Negatives (TN)
- C. False Positives (FP)
- D. False Negatives (FN)
Reveal answer
B. True Negatives (TN)
7. Which metric would you use to measure the proportion of negative cases that were correctly identified?
- A. Sensitivity
- B. Specificity
- C. Precision
- D. Accuracy
Reveal answer
B. Specificity
8. What is the main drawback of using accuracy as the sole metric for evaluating a classification model?
- A. It does not consider the balance between positive and negative classes.
- B. It requires a balanced dataset.
- C. It only considers the correctly predicted positive cases.
- D. It only considers the correctly predicted negative cases.
Reveal answer
A. It does not consider the balance between positive and negative classes.