1) Describe the criteria used to evaluate classification and prediction methods.

2) What is decision tree? With an example, briefly describe the algorithm for generating decision tree.

(or)

Discuss about classification by decision tree induction.

3) What is back propagation? Describe back propagation algorithm.

(Or)

Discuss about back propagation classification

4) What are Bayesian classifiers? With an example, describe how to predict a class label using naive Bayesian classification.

5) What is attribute selection measure? Briefly describe the attribute selection measures for decision tree induction?

6) Explain classification and prediction with and example.

7) Briefly outline the major steps for decision tree classification.

8) What is misclassification rate of a classifier? Describe sensitivity and specificity measures of a classifier.

9) Why naive Bayesian called “naive”? Briefly outline the major idea of naïve Bayesian classification?

(or)

What is bayes theorem? Explain about naïve Bayesian classification ………..6

10) Explain Bayesian classification.

11) Define regression. Briefly explain about linear, non- linear and multiple regressions.

12) Given a decision tree, you have the option of i) converting the decision tree to rules and then pruning the resulting rules (or) ii) pruning the decision tree and then converting the pruning tree to rules. What advantages does former option have over later one? Explain.

13) Can any idea from association rule mining be applied be applied to classification? Explain.

14) What is classification? What is prediction? Describe issues regarding classification and prediction.

15) What is linear regression? Give an example of linear regration using the method of least squares.

16) Explain Bayesian belief networks. How does a Bayesian belief network train?

17) Explain K-nearest neighbor classifier, Case based reasoning, genetic algorithms, Rough set approach and fuzzy set approaches.

18) What is prediction? Explain regression models.

19) What is classifier accuracy? Explain in detail about it.

20) Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of samples to evaluate pruning?

21) How rough set approach and fuzzy set approaches are useful for classification? Explain.

22) Explain data classification process with a neat diagram.

23) Explain classifier accuracy

22) The following table consists of training data from an employee database. The data have been generalized. For a given row entry , count represents the number of data tuples having the values for department ,status, age, and salary given in that below:

/uploads/1/1/2/5/11254582/untitled.png

Given a data sample with values “systems”, “junior” and “26….30” for the attributes departments, status, and age respectively. What would a naïve Bayesian classification of the salary for the sample be?

2) What is decision tree? With an example, briefly describe the algorithm for generating decision tree.

(or)

Discuss about classification by decision tree induction.

3) What is back propagation? Describe back propagation algorithm.

(Or)

Discuss about back propagation classification

4) What are Bayesian classifiers? With an example, describe how to predict a class label using naive Bayesian classification.

5) What is attribute selection measure? Briefly describe the attribute selection measures for decision tree induction?

6) Explain classification and prediction with and example.

7) Briefly outline the major steps for decision tree classification.

8) What is misclassification rate of a classifier? Describe sensitivity and specificity measures of a classifier.

9) Why naive Bayesian called “naive”? Briefly outline the major idea of naïve Bayesian classification?

(or)

What is bayes theorem? Explain about naïve Bayesian classification ………..6

10) Explain Bayesian classification.

11) Define regression. Briefly explain about linear, non- linear and multiple regressions.

12) Given a decision tree, you have the option of i) converting the decision tree to rules and then pruning the resulting rules (or) ii) pruning the decision tree and then converting the pruning tree to rules. What advantages does former option have over later one? Explain.

13) Can any idea from association rule mining be applied be applied to classification? Explain.

14) What is classification? What is prediction? Describe issues regarding classification and prediction.

15) What is linear regression? Give an example of linear regration using the method of least squares.

16) Explain Bayesian belief networks. How does a Bayesian belief network train?

17) Explain K-nearest neighbor classifier, Case based reasoning, genetic algorithms, Rough set approach and fuzzy set approaches.

18) What is prediction? Explain regression models.

19) What is classifier accuracy? Explain in detail about it.

20) Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of samples to evaluate pruning?

21) How rough set approach and fuzzy set approaches are useful for classification? Explain.

22) Explain data classification process with a neat diagram.

23) Explain classifier accuracy

22) The following table consists of training data from an employee database. The data have been generalized. For a given row entry , count represents the number of data tuples having the values for department ,status, age, and salary given in that below:

/uploads/1/1/2/5/11254582/untitled.png

Given a data sample with values “systems”, “junior” and “26….30” for the attributes departments, status, and age respectively. What would a naïve Bayesian classification of the salary for the sample be?