INTRODUCTION TO MACHINE LEARNING Week 7
Session: JAN-APR 2023
Course Name: Introduction to Machine Learning
Course Link: Click Here
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q1. For the given confusion matrix, compute the recall
a. 0.73
b. 0.7
c. 0.6
d. 0.67
e. 0.78
f. None of the above
Answer: d. 0.67
Q2. You have 2 multi-class classifiers A and B. A has accuracy = 0% and B has accuracy = 50%. Which classifier is more useful?
a. A
b. B
c. Both are equally good
d. Depends on the number of classes
Answer: d. Depends on the number of classes
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q3. For large datasets, we should always be choosing large k while doing k − fold cross validation to get better performance on test set.
a. True
b. False
Answer: b. False
Q4. We have a dataset with 1000 samples and 5 classes for classification. What would be the training size for a 20 fold cross validation?
a. 50
b. 200
c. 800
d. 950
Answer: d. 950
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q5. Which of the following are true?
TP – True Positive, TN – True Negative, FP – False Positive, FN – False Negative
a. Precision=TP/TP+FP
b. Recall=TP/TP+FN
c. Accuracy=2(TP+TN)/TP+TN+FP+FN
d. Recall=FP/TP+FP
Answer: a, b
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q6. In the ROC plot, what are the quantities along x and y axes respectively?
a. Precision, Recall
b. Recall, Precision
c. True Positive Rate, False Positive Rate
d. False Positive Rate, True Positive Rate
e. Specificity, Sensitivity
f. True Positive, True Negative
g. True Negative, True Positive
Answer: d. False Positive Rate, True Positive Rate
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q7. How does bagging help in improving the classification performance?
a. If the parameters of the resultant classifiers are fully uncorrelated (independent), then bagging is inefficient.
b. It helps reduce variance
c. If the parameters of the resultant classifiers are fully correlated, then bagging is inefficient.
d. It helps reduce bias
Answer: b, c
Q8. Which method among bagging and stacking should be chosen in case of limited training data? and What is the appropriate reason for your preference?
a. Bagging, because we can combine as many classifier as we want by training each on a different sample of the training data
b. Bagging, because we use the same classification algorithms on all samples of the training data
c. Stacking, because we can use different classification algorithms on the training data
d. Stacking, because each classifier is trained on all of the available data
Answer: d. Stacking, because each classifier is trained on all of the available data
Q9. Which of the following statements are false when comparing Committee Machines and Stacking
a. Committee Machines are, in general, special cases of 2-layer stacking where the second- layer classifier provides uniform weightage.
b. Both Committee Machines and Stacking have similar mechanisms, but Stacking uses different classifiers while Committee Machines use similar classifiers.
c. Committee Machines are more powerful than Stacking
d. Committee Machines are less powerful than Stacking
Answer: b, c
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
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Session: JUL-DEC 2022
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Course Name: INTRODUCTION TO MACHINE LEARNING
Link to Enroll: Click Here
Q1. You have 2 binary classifiers A and B. A has accuracy=0% and B has accuracy=50%. Which classifier is more useful?
a. A
b. B
c. Both are good
d. Cannot say
Answer: c. Both are good
Q2. You have 2 multi-class classifiers A and B. A has accuracy=0% and B has accuracy=50%. Which classifier is more useful?
a. A
b. B
c. Both are good
d. Cannot say
Answer: d. Cannot say
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q3. Using the bootstrap approach for sampling, the new dataset will have _________ of the original samples on expectation.
a. 50.0%
b. 56.8%
c. 63.2%
d. 73.6%
Answer: a. 50.0%
Q4. You have a special case where your data has 10 classes and is sorted according to target labels. You attempt 5-fold cross validation by selecting the folds sequentially. What can you say about your resulting model?
a. It will have 100% accuracy.
b. It will have 0% accuracy.
c. Accuracy will depend on how good the model does.
d. Accuracy will depend on the compute power available for training.
Answer: d. Accuracy will depend on the compute power available for training.
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q5. Given the following information
What is the precision and recall?
a. 0.5, 0.4375
b. 0.7, 0.636
c. 0.6, 0.636
d. 0.7, 0.4375
e. None of the above
Answer: e. None of the above
Q6. AUC for your newly trained model is 0.5. Is your model prediction completely random?
a. Yes
b. No
c. ROC curve is needed to derive this conclusion
d. Cannot be determined even with ROC
Answer: d. Cannot be determined even with ROC
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q7. What is the effect of using bagging on weak classifiers for variance?
a. Increases variance
b. Reduces variance
c. Does not change
Answer: a. Increases variance
Q8. You are building a model to detect cancer. Which metric will you prefer for evaluating your model?
a. Accuracy
b. Sensitivity
c. Specificity
d. MSE
Answer: c. Specificity
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
Q9. You are building a model to detect a mild medical condition for which further testing costs are extremely expensive. Which metric will you prefer for evaluating your model?
a. Accuracy
b. Sensitivity
c. Specificity
d. MSE
Answer: d. MSE
Q10. A: Boosting takes many weak learners and combines them into a strong learner.
B: Boosting determines the proportion of importance each weak learner should be assigned and weighs its prediction by it and combines them to make the final prediction.
a. A is True. B is True. B is the correct explanation for A.
b. A is True. B is True. B is not the correct explanation for A.
c. A is True. B is False.
d. Both A and B are False.
Answer: c. A is True. B is False.
These are Introduction to Machine Learning Week 7 Assignment 7 Answers
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