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- 1. Multiple Choice
It is possible to design a Linear regression algorithm using a neural network?
TRUE
FALSE
- 2. Multiple Choice
Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
AUC-ROC
Accuracy
Logloss
Mean-Squared-Error
- 3. Multiple Choice
Lasso Regularization can be used for variable selection in Linear Regression.
True
False
- 4. Multiple Choice
Which of the following is true about Residuals ?
Lower is better
Higher is better
A or B depend on the situation
None of these
- 5. Multiple Choice
Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data.You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.
Which of the following is true for X1?
Relation between the X1 and Y is weak
Relation between the X1 and Y is strong
Relation between the X1 and Y is neutral
Correlation can’t judge the relationship
- 6. Multiple Choice
Looking at above two characteristics, which of the following option is the correct for Pearson correlation between V1 and V2?
If you are given the two variables V1 and V2 and they are following below two characteristics.
1. If V1 increases then V2 also increases
2. If V1 decreases then V2 behavior is unknown
Pearson correlation will be close to 1
Pearson correlation will be close to -1
Pearson correlation will be close to 0
None of these
- 7. Multiple Choice
Which of the following offsets, do we use in linear regression’s least square line fit? Suppose horizontal axis is independent variable and vertical axis is dependent variable.
Vertical offset
Perpendicular offset
Both, depending on the situation
None of above
- 8. Multiple Choice
We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. Which of the following is/are true about Normal Equation?
1. We don’t have to choose the learning rate
2. It becomes slow when number of features is very large 3. Thers is no need to iterate
1 and 2
1 and 3
2 and 3
1,2 and 3
- 9. Multiple Choice
Which of the following statement is true about outliers in Linear regression?
Linear regression is sensitive to outliers
Linear regression is not sensitive to outliers
Can’t say
None of these
- 10. Multiple Choice
Describe the correlation in the graph shown.
Strong Negative
No Correlation
Weak Negative
Strong Positive
- 11. Multiple Choice
Is Logistic regression a supervised machine learning algorithm?
True
False
- 12. Multiple Choice
Is Logistic regression mainly used for Regression?
True
False
- 13. Multiple Choice
Is it possible to design a logistic regression algorithm using a Neural Network Algorithm?
True
False
- 14. Multiple Choice
Is it possible to apply a logistic regression algorithm on a 3-class Classification problem?
True
False
- 15. Multiple Choice
Which of the following methods do we use to best fit the data in Logistic Regression?
Least Square Error
Maximum Likelihood
Jaccard distance
Both A and B
- 16. Multiple Choice
Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?
AUC-ROC
Accuracy
Logloss
Mean-Squared-Error
- 17. Multiple Choice
Standardisation of features is required before training a Logistic Regression.
True
False
- 18. Multiple Choice
Which of the following option is true?
Linear Regression errors values has to be normally distributed but in case of Logistic Regression it is not the case
Logistic Regression errors values has to be normally distributed but in case of Linear Regression it is not the case
Both Linear Regression and Logistic Regression error values have to be normally distributed
Both Linear Regression and Logistic Regression error values have not to be normally distributed
- 19. Multiple Choice
Is the cost function of logistic and linear regression same?
Yes
No
- 20. Multiple Choice
Logisitic regression will not work when the data is linearly separable if we move our data into higher dimension.
True
False
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