MCQs on Confusion Matrix! #24
A quick knowledge check!
Hello Everyone,
Welcome to another edition of this newsletter focused on MCQs on a very important topic in machine learning.
Confusion matrix is one of the most discussed topics in machine learning interviews. ML practitioners often take this topic very lightly and fail to answer even the very basic questions.
So, I have complied today’s newsletter with the objective of helping you assess your understanding of the confusion matrix basics.
The answers will be shared in the next edition.
Just in case, you want to have a quick overview of the confusion matrix before attempting these MCQs, check out the following link:
Good luck!
Please note that TP = True Positive, FP = False Positive, TN = True Negative, FN = False Negative
1. Which component of a confusion matrix represents the cases where the actual value is positive but the predicted value is negative?
a. True Positive
b. False Positive
c. True Negative
d. False Negative
2. Which component of a confusion matrix represents the cases where the actual value is negative but the predicted value is positive?
a. True Positive
b. False Positive
c. True Negative
d. False Negative
3. Which component of a confusion matrix represents the cases where both the actual value and the predicted value are positive?
a. True Positive
b. False Positive
c. True Negative
d. False Negative
4. Which component of a confusion matrix represents the cases where both the actual value and the predicted value are negative?
a. True Positive
b. False Positive
c. True Negative
d. False Negative
5. Which metric indicates model’s ability to capture all the positive instances correctly?
a. Accuracy
b. Sensitivity
c. Specificity
d. F1-score
6. Which metric indicates model’s ability to capture all the negative instances correctly?
a. Accuracy
b. Sensitivity
c. Specificity
d. F1-score
7. Which metric calculates the harmonic mean between precision and recall?
a. Accuracy
b. Sensitivity
c. Specificity
d. F1-score
8. What is the formula of calculating precision using a confusion matrix?
a. TP/(TP+FP)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
9. What is the formula of calculating recall using a confusion matrix?
a. TP/(TP+FP)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
10. What is the formula of calculating accuracy using a confusion matrix?
a. (TP+TN)/(TP+FP+TN+FN)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
11. What is the formula of calculating sensitivity in the context of a confusion matrix?
a. TP/(TP+FP)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
12. What is the formula of calculating specificity in the context of a confusion matrix?
a. TP/(TP+FP)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
13. How “True Positive Rate” is calculated in the context of a confusion matrix?
a. (TP+TN)/(TP+FP+TN+FN)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
14. How “True Negative Rate” is calculated in the context of a confusion matrix?
a. (TP+TN)/(TP+FP+TN+FN)
b. TP/(TP+FN)
c. TN/(TN+FP)
d. TN/(TN+FN)
15. What does sensitivity measures in a confusion matrix?
a. True Positive Rate (TPR)
b. False Positive Rate (FPR)
c. True Negative Rate (TNR)
d. False Negative Rate (FNR)
16. What does specificity measures in a confusion matrix?
a. True Positive Rate (TPR)
b. False Positive Rate (FPR)
c. True Negative Rate (TNR)
d. False Negative Rate (FNR)
17. What is the accuracy of a model with TP = 34, FP = 29, TN = 16, FN =21?
a. 40%
b. 50%
c. 65%
d. 67%
18. The first cell (left side, top corner) in a confusion matrix is always TP.
a. True
b. False
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See you soon!
-Kavita
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