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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