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# NPTEL Data Mining Assignment Answers 2023 -Week 8

Are you a student struggling with the Data Mining NPTEL Week 8 assignment? Look no further! In this article, we have compiled a set of hints and answers to help guide you through the assignment.

Make sure to give it a try on your own first, but use these hints as a helpful resource.

## NPTEL DATA MINING ASSIGNMENT ANSWERS 2023 -WEEK 8

Q1.Target variable in Regression is _________

Q2. Regression is used in:

Q3. Regression finds out the model parameters which produces the least square error between –

Answer: (b.) input value and target value

Q4. Consider x1, x2 to be the independent variables and y to be the dependent variable, which of the following represents a linear regression model?

Answer: (b.) y = a0 + a1x1 + a2x2

Q5. The linear regression model y = a0 + a1x is applied to the data in the table shown below. What is the value of the sum squared error function S(a0, a1), when a0 = 1, a1=2 ?

Q6. The linear regression model y = a0 + a1x is to be fitted to the data in the table shown below. What is the optimal regression model obtained by minimizing sum squared error?

Answer: (d.) y = 1.01 + 0.98x

Q7. In the figures below the training instances are described by dots. The blue dotted lines indicate the actual functions and the red lines indicate the regression model. Which of the following statement is correct?

Answer: (b.) Figure 1 represents underrfitting and Figure 2 represents overfitting

Q8. Find the eigenvalues of the following matrix

Q9. A time series prediction problem is often solved using?

Q10. In principal component analysis, the projected lower dimensional space corresponds to –

Answer: (b) eigenvectors of the data covariance matrix

Disclaimer: Please keep in mind that these answers are intended to serve as a reference for students. Our website does not guarantee the accuracy of the answers provided. We encourage all students to complete their assignments independently and use these answers as a supplement to their own understanding.

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