1. What are classification and prediction? Explain using suitable examples 2 to 9

2. Explain about the preprocessing steps for preparing data for classification and

prediction

3. List out the criteria for comparing classification and prediction methods 14

4. What is a decision tree? How and why decision trees are used in classification? 15 to

5. Explain the decision tree induction algorithm and illustrate it. 19 to 32

6. Explain about tree pruning 33 to 41

7. Explain the different methods used to make decision tree induction more

scalable

8. What are Bayesian classifiers? 51 and 52

9. Explain the Bayes’ theorem 53 to 56

10. Explain the working of naïve or simple Bayesian classifier with an illustration 57 to 64

11. Explain about Bayesian belief networks 65 to 69

12. Explain the possible scenarios in training or learning in Bayesian belief

networks

13. Write a brief note on neural networks and classification by backpropagation 75 and

14. Explain about backpropagation based learning a on feed forward multilayer

neural network

15. Write a note on designing the topology of a neural network 80 and 81

16. Write and explain the backpropagation algorithm 82 to 92

17. Illustrate the backpropagation algorithm 93 to 95

18. Analyze the backpropagation algorithm in terms of interpretability 96 to 99

19. Explain different methods for applying the concepts of association rule mining

for classification

20. Write about k‐nearest neighborhood classification 110 to 113

21. Write about Case‐based reasoning in classification 114 to 116

22. Write about Genetic algorithms for classification 117 to 119

23. Write about Rough set in classification 120 to 123

24. Write about Fuzzy set approach to classification 124 to 127

25. Explain about prediction of continuous values modeled by linear and multiple

regression

26. Explain about prediction of continuous values modeled by nonlinear regression 27. Write a short on logistic regression and Poisson regression 136 to 138

28. Explain the holdout and cross‐validation techniques for assessing classifier

accuracy

29. Explain the different methods for improving classifier accuracy 144 to 148

30. Explain about sensitivity and specificity measures to judge a classifier 149 to 153

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