**NPTEL Introduction to Machine Learning Assignment 8 Answers 2022:-** In This article, we have provided the answers of Introduction to Machine Learning Assignment 8 You must submit your assignment to your own knowledge.

## About Introduction To Machine Learning

With the increased availability of data from varied sources, there has been increasing attention paid to the various data-driven disciplines such as analytics and machine learning. In this course, we intend to introduce some of the basic concepts of machine learning from a mathematically well-motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.**CRITERIA TO GET A CERTIFICATE**

Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score**YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.**

Introduction to Machine Learning | Answers |

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Assignment 10 | NA |

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## NPTEL Introduction to Machine Learning Assignment 8 Answers 2022 {July β Dec}

**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?

Answer:-

**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?

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:-

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**3. 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:-

**4. 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:-

**5. 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:-

**6. 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:-

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**7. 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:-

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## NPTEL Introduction to Machine Learning Assignment 8 Answers 2022 {Jan – June}

**Q1. Consider the two statements:**Statement 1: Gradient Boosted Decision Trees can overfit easily.

Statement 2: It is easy to parallelize Gradient Boosted Decision Trees.Which of these are true?

a. Both the statements are True.

b. Statement 1 is true, and statement 2 is false.

c. Statement 1 is false, and statement 2 is true.

d. Both the statements are false.

**Answer:- b. Statement 1 is true, and statement 2 is false. **

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**Q2.** 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, itβs 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: b. AdaBoost**

**Q3.** Which of the following algorithms donβt use learning rate as a hyperparameter?

- Random Forests
- Adaboost
- KNN
- PCA

**Answer: a, c, d**

**Q4.** Consider the following data for 500 instances of home, 600 instances of office and 700 instances of factory type buildings

suppose a building has a balcony and power-backup but is not multi-storied. According to the Naive Bayes algorithm, it is of type

- Home
- Office
- Factory

**Answer: c**

**Q5.** A dataset with two classes is plotted below.

**Answer: b**

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**Q6.** Which of these statements is/are True about Random Forests?

**Answer:- b**

**Q7.** Consider the below dataset:

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**Q8.** 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?

**Answer: C**

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**NPTEL Introduction to Machine Learning Assignment 8 Answers 2022:-** In This article, we have provided the answers of Introduction to Machine Learning Assignment 8 You must submit your assignment to your own knowledge.