### NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6

**These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6 WEEK 6**

**These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6 WEEK 6**

Course Name: INTRODUCTION TO MACHINE LEARNING

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**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 the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6 WEEK 6**

**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 the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6 WEEK 6**

**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 the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6 WEEK 6**

**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. q^{2}

c. 2^{q-1}

d. 2^{q-1} – 1

**Answer: d. 2 ^{q-1} – 1**

**These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 6 WEEK 6**

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