# NPTEL Introduction to Machine Learning Assignment 6 Answers 2022

## 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.

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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.

## NPTEL Introduction to Machine Learning Assignment 6 Answers 2022 {July – Dec}

1. Which of the following properties are characteristic of decision trees?

a. Low bias
b. High variance
c. Lack of smoothness of prediction surfaces
d. Unbounded parameter set

`Answer:- a, b, c, d`

2. Consider the following dataset :

What is the initial entropy of Malignant?

a. 0.543
b. 0.9798
c. 0.8732
d. 1

`Answer:- b`

3. For the same dataset, what is the info gain of Vaccination?

a. 0.4763
b. 0.2102
c. 0.1134
d. 0.9355

`Answer:- a`

4. Consider the following statements:

Statement 1: Decision Trees are linear non-parametric models.
Statement 2: A decision tree may be used to explain the complex function learned by a neural network.

a. Both the statements are True.
b. Statement 1 is True, but Statement 2 is False.
c. Statement 1 is False, but Statement 2 is True.
d. Both the statements are False.

`Answer:- c`

5. Which of the following machine learning models can solve the XOR problem without any transformations on the input space?

a. Linear Perceptron
b. Neural Networks
b. Decision Trees
d. Logistic Regression

`Answer:- b, c`

6. Which of the following is/are major advantages of decision trees over other supervised learning techniques (Note that more than one choices may be correct)

a. Theoretical guarantees of performance
b. Higher performance
c. Interpretability of classifier
d. More powerful in its ability to represent complex functions

`Answer:- c`

7. Consider a dataset with only one attribute(categorical). Suppose there are q unordered values in this attribute. How many possible combinations are needed to find the best split-point for building the decision tree classifier?

a. q
b. q2
c. 2q-1
d. 2q-1 – 1

`Answer:- d`

## NPTEL Introduction to Machine Learning Assignment 6 Answers 2022:-

Q1. Which of the following is very interpretable?

Q2. Which of these models are non-parametric?

Q3. Entropy for a 50-50 split between two classes is:

Q4. Statement: Decision Tree is an unsupervised learning algorithm.
Reason: The splitting criterion use only the features of the data to calculate their respective measures

Q5. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are three training data points with the following outputs: 5, 7, 9.6, and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11.

The maximum value of the outputs of data points denotes the response of a branch. The original responses for data points along the two branches (left & right respectively) were response_left and, response_right and the new response after collapsing the node is response_new. What are the values for response left, response_right and response_new (numbers in the option are given in the same order)?

Q6. Which among the following split-points for the feature1 would give the best split according to the information gain measure?

Q7. For the same dataset, which among the following split-points for feature2 would give the best split according to the gini index measure?

Q8. Consider a dataset with only one attribute(categorical). Suppose, there are 10 unordered values in this attribute, how many possible combinations are needed to find the best split-point for building the decision tree classifier?