### NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3

### These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3 WEEK 3

Course Name: INTRODUCTION TO MACHINE LEARNING

Q1. For linear classification we use:
a. A linear function to separate the classes.
b. A linear function to model the data.
c. A linear loss.
d. Non-linear function to fit the data.

Answer: b. A linear function to model the data.

Q2. Logit transformation for Pr(X=1) for given data is S=[0,1,1,0,1,0,1]
a. 3/4
b. 4/3
c. 4/7
d. 3/7

These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3 WEEK 3

Q3. The output of binary class logistic regression lies in this range.
a. [−∞,∞]
b. [−1,1]
c. [0,1]
d. [−∞,0]

These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3 WEEK 3

Q4. If log(1−p(x)1+p(x))=β0+βxlog What is p(x)p(x)?

Q5. Logistic regression is robust to outliers. Why?
a. The squashing of output values between [0, 1] dampens the affect of outliers.
b. Linear models are robust to outliers.
c. The parameters in logistic regression tend to take small values due to the nature of the problem setting and hence outliers get translated to the same range as other samples.
d. The given statement is false.

Answer: d. The given statement is false.

Q6. Aim of LDA is (multiple options may apply)
a. Minimize intra-class variability.
b. Maximize intra-class variability.
c. Minimize the distance between the mean of classes
d. Maximize the distance between the mean of classes

Answer: b. Maximize intra-class variability.

Q7. We have two classes in our dataset with mean 0 and 1, and variance 2 and 3.
a. LDA may be able to classify them perfectly.
b. LDA will definitely be able to classify them perfectly.
c. LDA will definitely NOT be able to classify them perfectly.
d. None of the above.

Answer: a. LDA may be able to classify them perfectly.

These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3 WEEK 3

Q8. We have two classes in our dataset with mean 0 and 5, and variance 1 and 2.
a. LDA may be able to classify them perfectly.
b. LDA will definitely be able to classify them perfectly.
c. LDA will definitely NOT be able to classify them perfectly.
d. None of the above.

Answer: b. LDA will definitely be able to classify them perfectly.

Q9. For the two classes ’+ and ’-’ shown below.
While performing LDA on it, which line is the most appropriate for projecting data points?

a. Red
b. Orange
c. Blue
d. Green

These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3 WEEK 3

Q10. LDA assumes that the class data is distributed as:
a. Poisson
b. Uniform
c. Gaussian
d. LDA makes no such assumption.

Answer: d. LDA makes no such assumption.

These are the solutions of NPTEL INTRODUCTION TO MACHINE LEARNING ASSIGNMENT 3 WEEK 3

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