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# 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

2. Logarithmic Loss is also known as ?

A. Gamma loss
B. Theta loss
C. Log loss
D. None of the above

3. What is the full form of AUC?

A. Area Undo Curve
B. Area Under Curve
C. Area Under Composition
D. Area Under Cover

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

5. What is the range of F1 Score?

A. 0 to 1
B. 0 to 10
C. 0 to 100
D. 0 to 1000

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

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

A. RMSE
B. RMSLE
C. MSE
D. MAE

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

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

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