Question Bank – Classification Module

1. Consider the data obtained by a real estate agency. Apply decision tree classifier and hence classify following tuple, <Very high, Old>

No.	Income	Age	Own house
1	Very high	Young	Yes
2	High	Medium	Yes
3	Low	Young	Rented
4	High	Medium	Yes
5	Very high	Medium	Yes
6	Medium	Young	Yes
7	High	Old	Yes
8	Medium	Medium	Rented
9	Low	Medium	Rented
10	Low	Old	Rented
11	High	Young	Yes

2) Using the given training dataset classify the following tuple using Naïve Bayes Algorithm:

<Homeowner: No, Marital Status: Married, Job experience:3>

Homeowner	Marital Status	Job experience (in years)	Defaulted
Yes	Single	3	No
No	Married	4	No
No	Single	5	No
Yes	Married	4	No
No	Divorced	2	Yes
No	Married	4	No
Yes	Divorced	2	No
No	Married	3	Yes
No	Married	3	No
Yes	Single	2	Yes

3) A sample training set for stock market is given below. Profit is the class attribute and value is based on age, contest and type. Apply decision tree induction for this dataset to generate classification rules.

Age	Contest	Type	Profit
Old	Yes	Swr	Down
Old	No	Swr	Down
Old	No	Hwr	Down
Mid	Yes	Swr	Down
Mid	Yes	Hwr	Down
Mid	No	Hwr	Up
Mid	No	Swr	Up
New	Yes	Swr	Up
New	No	Hwr	Up
New	No	Swr	Up

4) Given the training data set for height classification of TE students. Classify the tuple t=<Avinash, male,1.95> using Bayesian classification

Person ID	Name	Gender	Height	Class
1	Arti	F	1.6	Short
2	Jatin	M	2	Tall
3	Madhuri	F	1.9	Medium
4	Manisha	F	2.1	Tall
5	Shilpa	F	1.7	Short
6	Dinesh	M	1.85	Medium
7	Divya	F	1.6	Short
8	Tushar	M	1.7	Short
9	Rohan	M	2.2	Tall

5) Suppose we have a binary classifier that predicts whether an email is spam (Positive) or not spam (Negative). We evaluated the classifier on a dataset of 100 emails, and the results are as follows: The classifier correctly predicted 15 emails as spam, and they were indeed spam emails. The classifier incorrectly classified 5 emails as spam, but they were not spam; these were false alarms. The classifier correctly identified 70 emails as not spam, and they were genuinely not spam. The classifier missed 10 spam emails, incorrectly predicting them as not spam. Draw the confusion matrix and calculate Accuracy, Precision, and Recall for the Confusion matrix

6) Using the given training dataset for credit transaction. Classify a new transaction with (income= Medium and Credit=Good) using Naïve Bayes Algorithm:

<Homeowner: No, Marital Status: Married, Job experience:3>

Transaction	Income	Credit	Decision
1	Very high	Excellent	Authorize
2	High	Good	Authorize
3	Medium	Excellent	Authorize
4	High	Good	Authorize
5	Very high	Good	Authorize
6	Medium	Excellent	Authorize
7	High	Bad	Request Id
8	Medium	Bad	Request Id
9	High	Bad	Reject
10	Low	Bad	Call Police

7) Apply decision tree classifier to generate the tree

Instance	A1	A2	A3	Classification
1	True	Hot	High	No
2	True	Hot	High	No
3	False	Hot	High	Yes
4	False	Cool	Normal	Yes
5	False	Cool	Normal	Yes
6	True	Cool	High	No
7	True	Hot	High	No
8	True	Hot	Normal	Yes
9	False	Cool	Normal	Yes

8) Apply Naïve Bayes Classifier and classify the tuple:

<Age=Youth, Income= Medium, Student= Yes, Credit Rating= Fair> for the given dataset.

RID	Age	Income	Student	Credit Rating	Class: Buys Computer
1	Youth	High	No	Fair	No
2	Youth	High	No	Excellent	No
3	Middle-aged	High	No	Fair	Yes
4	Senior	Medium	No	Fair	Yes
5	Senior	Low	Yes	Fair	Yes
6	Senior	Low	Yes	Excellent	No
7	Middle-aged	Low	Yes	Excellent	Yes
8	Youth	Medium	No	Fair	No
9	Youth	Low	Yes	Fair	Yes
10	Senior	Medium	Yes	Fair	Yes
11	Youth	Medium	Yes	Excellent	Yes
12	Middle-aged	Medium	No	Excellent	Yes
13	Middle-aged	High	Yes	Fair	Yes
14	Senior	Medium	No	Excellent	No

9) Calculate accuracy for the following confusion matrix.

Fraud classes	Yes	No	Total
Yes	150	200	350
No	150	9500	9650
Total	300	9700	10000

10) Calculate recall, accuracy, precision

N=200	Predicted to be “Spam”	Predicted to be “Not Spam”
Actual “Spam”	120	30
Actual “Not Spam”	10	40