Module 3 PDF - Machine Learning Past Paper - VARDHAMAN COLLEGE OF ENGINEERING 2024-2025

VARDHAMAN COLLEGE OF ENGINEERING (AUTONOMOUS) Affiliated to JNTUH, Approved by AICTE, Accredited by NAAC with A++ Grade, ISO 9001:2015 Certified Kacharam, Shamshabad, Hyderabad – 501218, Telangana, India...

VARDHAMAN COLLEGE OF ENGINEERING (AUTONOMOUS) Affiliated to JNTUH, Approved by AICTE, Accredited by NAAC with A++ Grade, ISO 9001:2015 Certified Kacharam, Shamshabad, Hyderabad – 501218, Telangana, India COURSE STRUCTURE A8703 - Machine Learning B. Tech III-SEM -I A.Y (2024-25) Short Question: 1. What is prior probability? Give an example. 2. What is Naïve Bayes classifier? Why is it named so? 3. Write any two features of Bayesian learning methods. 4. Explain how Naïve Bayes classifier is used for  Text classification  Spam filtering  Market sentiment analysis 5. What is supervised learning? Why it is called so? 6. Define kernel in the SVM model. 7. Write notes on:  validation error in the kNN algorithm  choosing k value in the kNN algorithm  inductive bias in a decision tree 8. Define information gain in a decision tree. 9. What are the characteristics of ID3 algorithm? 10. Write any three weaknesses of the decision tree method. 11. Explain, in brief, the random forest model? 12. Define slope in a linear regression. 13. Define sum of squares due to error in multiple linear regression. 14. What is simple linear regression? Give one example. 15. What is a dependent variable and an independent variable in a linear equation? 16. What is polynomial regression? Long Question: 1. Discuss the error rate and validation error in the kNN algorithm. 2. Discuss the decision tree algorithm in detail. 3. Discuss the random forest model in detail. What are the features of random forest? 4. Explain the OLS algorithm with steps. 5. Explain polynomial regression model in detail with an example. 6. Explain slope, linear positive slope, and linear negative slope in a graph along with various conditions leading to the slope. 7. Explain, in brief, the SVM model. Find whether Bob has a cold (hypotheses) given that he sneezes (the evidence) i.e., calculate P(h | D) and P(h | ~D). Suppose that we know / given the following.  P(h) = P (Bob has a cold) = 0.2  P(D | h) = P(Bob was observed sneezing| Bob has a cold) = 0.75  P(D | ~h)= P(Bob was observed sneezing | Bob does not have a cold) = 0.2 8. A patient takes a lab test and the result comes back positive. The test returns a correct positive result in only 98% of the cases in which the disease is actually present, and the test returns a correct negative result in only 97% of the cases in which the disease is not present. Furthermore, 0.008 of the entire population have this cancer. Does the patient have cancer or not?  9. (a) What is the entropy of this collection of training example with respect to the target function classification? (b) What is the information gain of a2 relative to these training example. 10. Given this training data, use the naïve Bayes classifier to classify assigns the target value PlayTennis for the following new instance “Sunny, Cool, High, strong”.