INTRODUCTION TO MACHINE LEARNING Week 8
Session: JULY-DEC 2023
Course Name: Introduction to Machine Learning
Course Link: Click Here
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q1. The figure below shows a Bayesian Network with 9 variables, all of which are binary.
Which of the following is/are always true for the above Bayesian Network?
P(A,B|G)=P(A|G)P(B|G)
P(A,I)=P(A)P(I)
P(B,H|E,G)=P(B|E,G)P(H|E,G)
P(C|B,F)=P(C|F)
Answer: P(A,I)=P(A)P(I)
Q2. Consider the following data for 20 budget phones, 30 mid-range phones, and 20 high-end phones:
Consider a phone with 2 SIM card slots and NFC but no 5G compatibility. Calculate the probabilities of this phone being a budget phone, a mid-range phone, and a high-end phone using the Naive Bayes method. The correct ordering of the phone type from the highest to the lowest probability is?
Budget, Mid-Range, High End
Budget, High End, Mid-Range
Mid-Range, High End, Budget
High End, Mid-Range, Budget
Answer: Mid-Range, High End, Budget
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q3. A dataset with two classes is plotted below.
Does the data satisfy the Naive Bayes assumption?
Yes
No
The given data is insufficient
None of these
Answer: No
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q4. A company hires you to look at their classification system for whether a given customer would potentially buy their product. When you check the existing classifier on different folds of the training set, you find that it manages a low accuracy of usually around 60%. Sometimes, it’s barely above 50%.
With this information in mind, and without using additional classifiers, which of the following ensemble methods would you use to increase the classification accuracy effectively?
Committee Machine
AdaBoost
Bagging
Stacking
Answer: AdaBoost
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q5. Which of the following algorithms don’t use learning rate as a hyperparameter?
Random Forests
Adaboost
KNN
PCA
Answer: A, C
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q6. Consider the two statements:
Statement 1: Bayesian Networks need not always be Directed Acyclic Graphs (DAGs)
Statement 2: Each node in a bayesian network represents a random variable, and each edge represents conditional dependence.
Which of these are true?
Both the statements are True.
Statement 1 is true, and statement 2 is false.
Statement 1 is false, and statement 2 is true.
Both the statements are false.
Answer: Both the statements are True.
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q7. A dataset with two classes is plotted below.
Does the data satisfy the Naive Bayes assumption?
Yes
No
The given data is insufficient
None of these
Answer: Yes
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q8. Consider the below dataset:
Suppose you have to classify a test example “The ball won the race to the boundary” and are asked to compute P(Cricket |“The ball won the race to the boundary”), what is an issue that you will face if you are using Naive Bayes Classifier, and how will you work around it? Assume you are using word frequencies to estimate all the probabilities.
There won’t be a problem, and the probability of P(Cricket |“The ball won the race to the boundary”) will be equal to 1.
Problem: A few words that appear at test time do not appear in the dataset.
Solution: Smoothing.
Problem: A few words that appear at test time appear more than once in the dataset.
Solution: Remove those words from the dataset.
None of these
Answer: Problem: A few words that appear at test time do not appear in the dataset.
Solution: Smoothing.
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
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Session: JAN-APR 2023
Course Name: Introduction to Machine Learning
Course Link: Click Here
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q1. The Naive Bayes classifier makes the assumption that the __are independent given the ___.
a. features, class labels
b. class labels, features
c. features, data points
d. there is no such assumption
Answer: a. features, class labels
Q2. Can the decision boundary produced by the Naive Bayes algorithm be non-linear?
a. no
b. yes
Answer: b. yes
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q3. A major problem of using the one vs. rest multi-class classification approach is:
a. class imbalance
b. increased time complexity
Answer: a. class imbalance
Q4. Consider the problem of learning a function X→Y, where Y is Boolean. X is an input vector (X1,X2)
, where X1 is categorical and takes 3 values, and X2 is a continuous variable (normally distributed). What would be the minimum number of parameters required to define a Naive Bayes model for this function?
a. 8
b. 10
c. 9
d. 5
Answer: c. 9
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q5. In boosting, the weights of data points that were miscalssified are __ as training progresses.
a. decreased
b. increased
c. first decreased and then increased
d. kept unchanged
Answer: b. increased
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q6. In a random forest model let m<<p be the number of randomly selected features that are used to identify the best split at any node of a tree. Which of the following are true? (p is the original number of features) (Multiple options may be correct)
a. increasing m reduces the correlation between any two trees in the forest
b. decreasing m reduces the correlation between any two trees in the forest
c. increasing m increases the performance of individual trees in the forest
d. decreasing m increases the performance of individual trees in the forest
Answer: b, c
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q7. Consider the following graphical model, which of the following are false about the model? (multiple options may be correct)
a. A is independent of B when C is known
b. D is independent of A when C is known
c. D is not independent of A when B is known
d. D is not independent of A when C is known
Answer: a, b
Q8. Consider the Bayesian network given in the previous question. Let ‘A’, ‘B’, ‘C’, ‘D’and ‘E’denote the random variables shown in the network. Which of the following can be inferred from the network structure?
a. ‘A’causes ‘D’
b. ‘E’causes ‘D’
c. ‘C’causes ‘A’
d. options (a) and (b) are correct
e. none of the above can be inferred
Answer: e. none of the above can be inferred
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
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Session: JUL-DEC 2022
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Course Name: INTRODUCTION TO MACHINE LEARNING
Link to Enroll: Click Here
Q1. The figure below shows a Bayesian Network with 9 variables, all of which are binary.
Which of the following is/are always true for the above Bayesian Network?
a. P(A,B|G)=P(A|G)P(B|G)P(A,B|G)=P(A|G)P(B|G)
b. P(A,I)=P(A)P(I)P(A,I)=P(A)P(I)
c. P(B,H|E,G)=P(B|E,G)P(H|E,G)P(B,H|E,G)=P(B|E,G)P(H|E,G)
d. P(C|B,F)=P(C|F)P(C|B,F)=P(C|F)
Answer: c, d
Q2. Consider the following data for 20 budget phones, 30 mid-range phones, and 20 high-end phones:
Consider a phone with 2 SIM card slots and NFC but no 5G compatibility. Calculate the probabilities of this phone being a budget phone, a mid-range phone, and a high-end phone using the Naive Bayes method. The correct ordering of the phone type from the highest to the lowest probability is?
a. Budget, Mid-Range, High End
b. Budget, High End, Mid-Range
c. Mid-Range, High End, Budget
d. High End, Mid-Range, Budget
Answer: c. Mid-Range, High End, Budget
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q3. Consider the following dataset where outlook, temperature, humidity, and wind are independent features, and play is the dependent feature.
Find the probability that the student will not play given that x = (Outlook=sunny, Temperature=66, Humidity=90, Windy=True) using the Naive Bayes method. (Assume the continuous features are represented as Gaussian distributions).
a. 0.0001367
b. 0.0000358
c. 0.0000236
d. 1
Answer: c. 0.0000236
Q4. Which among Gradient Boosting and AdaBoost is less susceptible to outliers considering their respective loss functions?
a. AdaBoost
b. Gradient Boost
c. On average, both are equally susceptible.
Answer: b. Gradient Boost
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q5. How do you prevent overfitting in random forest models?
a. Increasing Tree Depth.
b. Increasing the number of variables sampled at each split.
c. Increasing the number of trees.
d. All of the above.
Answer: d. All of the above.
Q6. A dataset with two classes is plotted below.
Does the data satisfy the Naive Bayes assumption?
a. Yes
b. No
c. The given data is insufficient
d. None of these
Answer: a. Yes
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
Q7. Ensembling in random forest classifier helps in achieving:
a. reduction of bias error
b. reduction of variance error
c. reduction of data dimension
d. none of the above
Answer: c. reduction of data dimension
These are Introduction to Machine Learning Week 8 Assignment 8 Answers
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