NPTEL Data Analytics with Python Week 9 Assignment Answers 2025
1. State true or false: Statement: there is no difference between, E(y) = 0 + 1x and y = 0 + 1x + e , both are regression equations
- True
- False
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2. Which of the following statements is correct:
- Sensitivity in ROC analysis is called True Positive Rate(tpr)
- Specificity in ROC analysis is not called True Negative Rate (tnr)
- Specificity in ROC analysis is called True Positive Rate(tpr)
- Sensitivity in ROC analysis is called True Negative Rate (tnr)
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3. In ROC analysis when the Threshold value is Higher:
- Specificity decreases
- Sensitivity decreases
- Both a. and b.
- None of the above
Answer :-
4. Sensitivity in ROC analysis is defined as (TP = True Positive, FP = False Positive, TN = True Negative, FN = False Negative):
- FP / (FP+TN)
- FN/(TP+FN)
- TN / (TN+FP)
- TP / (TP+FN)
Answer :-
5. In ROC analysis, a classifier is called ‘good’ if it has __________
- Low TPR and Low FPR
- Low TPR and High FPR
- High TPR and Low FPR
- High TPR and High FPR
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6.
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7. State true or False: Precision is inversely proportional to recall
- True
- False
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8. State True or False: Standardization of features is not required before training a Logistic regression model
- True
- False
Answer :-
9. Which of the following option is true?
- Linear Regression errors values have to be normally distributed but in the case of Logistic Regression it is not the case
- Logistic Regression errors values have to be normally distributed but in the 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
Answer :-
10. Which of the following is true regarding the logistic function for any value “x”?
A. Logistic(x): is a logistic function of any number “x”
B. Logit(x): is a logit function of any number “x”
c. Logit_inv(x): is an inverse logit function of any number “x”
- Logistic(x) = Logit(x)
- Logistic(x) = Logit_inv(x)
- Logit_inv(x) = Logit(x)
- None of these
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