Assignment (Elaborated) DATA MINING TECHNIQUES (CS 63015)

Q1. Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned tree to rules. What advantage does (a) have over (b)? (0.75 Mark) Q2. See the following Figure and compute the true positive rate .TPR/ , false positive rate .FPR/, Precision and Accuracy. (1 Marks)Q3. Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, case-based reasoning). ( 1 Mark) Q4. The following decision tree has been created to predict what someone can do.Convert this tree to if then rules (0.25 Mark) b. Using the following testing data: i. Predict the class of each record (0.25 Mark) Parents Visiting Weather Money class Prediction 1 Yes Sunny Rich Shopping 2 Yes Windy Poor Cinema 3 No Windy Poor Play tennis 4 No Rainy Rich Stay in 5 Yes Rainy Poor Stay in 6 No Windy Rich Cinema ii. Calculate the accuracy of this model. (0.25 Mark) iii. Interpret the obtained result (0.25 Mark) iv. How we can improve the performance of the obtained model? (0.25Mark)Q1. Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned tree to rules. What advantage does (a) have over (b)? (0.75 Mark) Q2. See the following Figure and compute the true positive rate .TPR/ , false positive rate .FPR/, Precision and Accuracy. (1 Marks)Q3. Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, case-based reasoning). ( 1 Mark) Q4. The following decision tree has been created to predict what someone can do.Convert this tree to if then rules (0.25 Mark) b. Using the following testing data: i. Predict the class of each record (0.25 Mark) Parents Visiting Weather Money class Prediction 1 Yes Sunny Rich Shopping 2 Yes Windy Poor Cinema 3 No Windy Poor Play tennis 4 No Rainy Rich Stay in 5 Yes Rainy Poor Stay in 6 No Windy Rich Cinema ii. Calculate the accuracy of this model. (0.25 Mark) iii. Interpret the obtained result (0.25 Mark) iv. How we can improve the performance of the obtained model? (0.25Mark)