Classification and prediction Question

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