# Evaluation Metrics Questions

Evaluation Metrics MCQs : This section focuses on "Evaluation Metrics" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.

1. How do we calculate Classification Accuracy?

A. by calculating the ratio of correct predictions to the total number of input Samples

B. by calculating the ratio of total number of input Samples to the correct predictions

C. by calculating the ratio of wrong predictions to the total number of input Samples

D. by calculating the ratio of total number of input Samples to the wrong predictions

View Answer

Ans : A

Explanation: Classification accuracy is the accuracy we generally mean, whenever we use the term accuracy. We calculate this by calculating the ratio of correct predictions to the total number of input Samples.

2. Logarithmic Loss is also known as ?

A. Gamma loss

B. Theta loss

C. Log loss

D. None of the above

View Answer

Ans : C

Explanation: Logarithmic Loss is also known as Log loss

3. What is the full form of AUC?

A. Area Undo Curve

B. Area Under Curve

C. Area Under Composition

D. Area Under Cover

View Answer

Ans : B

Explanation: Full form of AUC is Area Under Curve

4. F1 Score is a harmonic mean between recall and precision?

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

View Answer

Ans : A

Explanation: F1 Score is a harmonic mean between recall and precision

5. What is the range of F1 Score?

A. 0 to 1

B. 0 to 10

C. 0 to 100

D. 0 to 1000

View Answer

Ans : A

Explanation: The range of F1 score is 0 to 1

6. The average distance between Predicted and original values is?

A. Mean Squared Error

B. Root Mean Square Error

C. Root Mean Squared Logarithmic Error

D. Mean Absolute Error

View Answer

Ans : D

Explanation: Mean Absolute Error(MAE) : It is the average distance between Predicted and original values.

7. _________ is used as an evaluation metric which helps us to achieve the above objective.

A. RMSE

B. RMSLE

C. MSE

D. MAE

View Answer

Ans : B

Explanation: RMSLE is used as an evaluation metric which helps us to achieve the above objective.

8. Confusion creates a N X N matrix Where N is?

A. number of objects

B. number of samples

C. random number

D. number of classes

View Answer

Ans : D

Explanation: Confusion Matrix : It creates a N X N matrix, where N is the number of classes or categories that are to be predicted

9. RMSE is a metric that can be obtained by just taking the square root of the MSE value.

A. Yes

B. No

C. Can be yes or no

D. Can not say

View Answer

Ans : A

Explanation: Yes, RMSE is a metric that can be obtained by just taking the square root of the MSE value.

10. R2 score is used to evaluate the performance of a ____________.

A. logistic regression model

B. polynomial regression model

C. linear regression model

D. bayesian regression model

View Answer

Ans : C

Explanation: R2 score is used to evaluate the performance of a linear regression model.

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