INTRODUCTION TO MACHINE LEARNING Week 6

Session: JAN-APR 2023

Course Name: Introduction to Machine Learning

Course Link: Click Here

These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q1. When building models using decision trees we essentially split the entire input space using
a. axis parallel hyper-rectangles
b. polynomials curves of order greater than two
c. polynomial curves of the same order as the length of decision tree
d. none of the above

Answer: a. axis parallel hyper-rectangles


Q2. In building a decision tree model, to control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?
a. it would, in general, help restrict the size of the trees
b. it has the potential to affect the performance of the resultant regression/classification model
c. it is computationally infeasible

Answer: a, b


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q3. Suppose we use the decision tree model for solving a multi-class classification problem. As we continue building the tree, w.r.t. the generalisation error of the model,
a. the error due to bias increases
b. the error due to bias decreases
c. the error due to variance increases
d. the error due to variance decreases

Answer: b, c


Q4. 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 3 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 average value of the outputs of data points denotes the response of a branch.

The original responses for data points 1along 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)?
a. 21.6, 40, 61.6
b. 7.2; 10; 8.8
c. 3, 4, 7
d. depends on the tree height.

Answer: b. 7.2; 10; 8.8


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q5. Consider the following dataset:

image 77

Which among the following split-points for the feature1 would give the best split according to the information gain measure?
a. 14.6
b. 16.05
c. 16.85
d. 17.35

Answer: b. 16.05


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q6 .For the same dataset, which among the following split-points for feature2 would give the best split according to the gini index measure?
a. 172.6
b. 176.35
c. 178.45
d. 185.4

Answer: a. 172.6


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q7. In which of the following situations is it appropriate to introduce a new category ’Missing’ for missing values? (multiple options may be correct)
a. When values are missing because the 108 emergency operator is sometimes attending a very urgent distress call.
b. When values are missing because the attendant spilled coffee on the papers from which the data was extracted.
c. When values are missing because the warehouse storing the paper records went up in flames and burnt parts of it.
d. When values are missing because the nurse/doctor finds the patient’s situation too urgent.

Answer: a, d


These are Introduction to Machine Learning Week 6 Assignment 6 Answers

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Session: JUL-DEC 2022

These are Introduction to Machine Learning Week 6 Assignment 6 Answers

Course Name: INTRODUCTION TO MACHINE LEARNING

Link to Enroll: Click Here


Q1. 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: b, c, d


Q2. Consider the following dataset :
What is the initial entropy of Malignant?

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

Answer: b. 0.9798


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q3. For the same dataset, what is the info gain of Vaccination?
a. 0.4763
b. 0.2102
c. 0.1134
d. 0.9355

Answer: b. 0.2102


Q4. 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. Statement 1 is False, but Statement 2 is True.


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q5. 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


Q6. 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: a, b, c ,d


These are Introduction to Machine Learning Week 6 Assignment 6 Answers


Q7. 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. 2q-1 – 1


These are Introduction to Machine Learning Week 6 Assignment 6 Answers

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These are Introduction to Machine Learning Week 6 Assignment 6 Answers