NPTEL Introduction To Machine Learning Week 8 Assignment Solutions

## NPTEL Introduction To Machine Learning Week 8 Assignment Answer 2023

**1. 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 :- For AnswerClick Here

**2. 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 :-For AnswerClick Here

**3. 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 :-For AnswerClick Here

**4. 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 :-For AnswerClick Here

**5. Which of the following algorithms don’t use learning rate as a hyperparameter?**

- Random Forests
- Adaboost
- KNN
- PCA

Answer :-For AnswerClick Here

**6. 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 :-For AnswerClick Here

**7. 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 :-For AnswerClick Here

**8. 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 :-For AnswerClick Here

Course Name | Introduction To Machine Learning |

Category | NPTEL Assignment Answer |

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