Reference no: EM13849372

1. In this part, we are going to build a decision tree classifier in MATLAB to predict the acceptability of cars. The dataset can be found in the CSV file car.csv. The first six columns are the attributes of the cars and the last column is class label denoting the evaluated car acceptability. The attribute information is shown in the following table:

Attribute	Description	Possible values
buying	Buying Price	Vhigh,high,med,low
Maint	Maintenance Price	Vhigh,high,med,low
Doors	No.of doors	2,3,4,5 more
Persons	Passenger capacity	2,4 more
Lug boot	Size of luggage boot	Small big med
Safety	Estimated safety of the car	Low ,med, high
Accept (class label)	Acceptability of the car	Unacc,acc,good,vgood

a. All the six attributes (except the class label) require encoding (i.e. transforms them into integral values). Propose an encoding scheme, and implement it into a MATLAB function to perform the encoding. The answer should include the followings:

i. Encoding scheme for each attribute (preferably presented as an encoding table)

ii. MATLAB function source codes implementing the encoding scheme in (i)

b. By using the first 75% of the tuples for training, and the remaining 25% for testing, build a MATLAB decision tree classifier to predict the acceptability of the cars in the testing dataset.

The answer should include the followings:

 i. MATLAB source codes building classifier, and car acceptability prediction using the testing dataset. Clear instructions must be given for the execution of your source codes, or marks will be deducted otherwise

ii. The decision tree built, accuracy, sensitivity, and specificity (i)) Submit your MATLAB source codes in separate MATLAB files. c. Why is the accuracy in

(b.ii) much lower than sensitivity and specificity?

2. Name two techniques in data pre-processing. Discuss their basic principles and illustrate one example for each technique discussed.

3. The following table presents the survey results about the preference of the mobile phone game apps.

Age	Gender	Occupation	Preferred type of game apps
29	F	Manager	Puzzle
25	M	Manager	Action
27	M	Student	Sports
17	M	Student	Action
23	M	Clerk	Puzzle
30	F	Clerk	Sports
14	M	Student	Puzzle
28	F	Clerk	Sports
22	M	Clerk	Action
36	M	manager	puzzle

Using the above table as the training dataset, build a decision tree by applying Hunt's algorithm. The class label is the preferred type of game apps. Age, gender, and occupation are the attributes. Split the attributes using multi-way split with GINI index. Show clearly your steps and calculations.