NPTEL Introduction to Machine Learning Assignment 3 Answer 2023

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NPTEL Introduction to Machine Learning Assignment 3 Answer 2023 – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

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NPTEL Introduction to Machine Learning Assignment

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1 point

Which of the following are differences between LDA and Logistic Regression?

 Logistic Regression is typically suited for binary classification, whereas LDA is directly applicable to multi-class problems

 Logistic Regression is robust to outliers whereas LDA is sensitive to outliers

 both (a) and (b)

 None of these

Answer :  C

1 point

We have two classes in our dataset. The two classes have the same mean but different variance.

 LDA can classify them perfectly.

 LDA can NOT classify them perfectly.

 LDA is not applicable in data with these properties

 Insufficient information

Answer : B 

1 point 

We have two classes in our dataset. The two classes have the same variance but different mean.

 LDA can classify them perfectly.

 LDA can NOT classify them perfectly.

 LDA is not applicable in data with these properties

 Insufficient information

Answer :  A

1 point

Given the following distribution of data points:

What method would you choose to perform Dimensionality Reduction?

 Linear Discriminant Analysis

 Principal Component Analysis

 Both LDA and/or PCA.

 None of the above.

Answer :  B

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1 point

If
log(1−p(x)1+p(x))=β0+βx���(1−�(�)1+�(�))=�0+��
What is p(x)�(�)?

p(x)=1+eβ0+βxeβ0+βx�(�)=1+�β0+β��β0+β�

p(x)=1+eβ0+βx1−eβ0+βx�(�)=1+�β0+β�1−�β0+β�

p(x)=eβ0+βx1+eβ0+βx�(�)=�β0+β�1+�β0+β�

p(x)=1−eβ0+βx1+eβ0+βx�(�)=1−�β0+β�1+�β0+β�

Answer :  p(x) = e^(β0+βx) / (1 + e^(β0+βx)).

1 point

For the two classes ’+’ and ’-’ shown below.

While performing LDA on it, which line is the most appropriate for projecting data points?

 Red

 Orange

 Blue

 Green

Answer :  C

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1 point

Which of these techniques do we use to optimise Logistic Regression:

 Least Square Error

 Maximum Likelihood

 (a) or (b) are equally good

 (a) and (b) perform very poorly, so we generally avoid using Logistic Regression

 None of these

Answer :  B

1 point

LDA assumes that the class data is distributed as:

 Poisson

 Uniform

 Gaussian

 LDA makes no such assumption.

Answer :  C

1 point

Suppose we have two variables, X� and Y� (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y=meX+c�=���+�. Suppose the samples of the variables X� and Y� are available to us. Is it possible to apply linear regression to this data to estimate the values of m� and c�?

 No.

 Yes.

 Insufficient information.

 None of the above.

Answer :  B

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1 point

What might happen to our logistic regression model if the number of features is more than the number of samples in our dataset?

 It will remain unaffected

 It will not find a hyperplane as the decision boundary

 It will over fit

 None of the above

Answer :  B

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