[Solved] ITE4005 Assignment 2-decision tree

1. Environment l OS: Windows, Mac OS, or Linux

l Languages: C++, Java, or Python (any version is ok)

2. Goal: Build a decision tree, and then classify the test set using it

The program must meet the following requirements: l Execution file name: dt.exe

• Execute the program with three arguments: training file name, test file name, output file name n Example:

Training file name=dt_train.txt, test file name=dt_test.txt, output file name=dt_result.txt If using python, you are allowed to use dt.py file instead of dy.exe.

• Dataset n We provide you with 2 datasets
  • Buy_computer: dt_train.txt, dt_test.txt
  • Car_evaluation: dt_train1.txt, dt_test1.txt n You need to make your program that can deal with any datasets n We will evaluate your program with other datasets.
• File format for a training set

[attribute_name_1]t[attribute_name_2]t [attribute_name_n]

[attribute_1]t[attribute_2]t [attribute_n]

[attribute_1]t[attribute_2]t [attribute_n]

[attribute_1]t[attribute_2]t [attribute_n]

• [attribute_name_1] ~ [attribute_name_n]: n attribute names
• [attribute_1] ~ [attribute_n-1]
  • n-1 attribute values of the corresponding tuple
  • All the attributes are categorical (not continuous-valued) n [attribute_n]: a class label that the corresponding tuple belongs to n Example 1 (data_train.txt):

Figure 1. An example of the first training set.

• Example 2 (data_train1.txt):

Figure 2. An example of the second training set.

• Title: car evaluation database
• Attribute values l Buying: vhigh, high, med, low l Maint: vhigh, high, med, low l Doors: 2, 3, 4, 5more l Persons: 2, 4, more l Lug_boot: small, med, big l Safety: low, med, high
• Class labels: unacc, acc, good, vgood
• Number of instances: training set 1,382; test set 346

• Attribute selection measure: information gain, gain ratio, or gini index l File format for a test set

[attribute_name_1]t[attribute_name_2]t [attribute_name_n-1]

[attribute_1]t[attribute_2]t [attribute_n-1]

[attribute_1]t[attribute_2]t [attribute_n-1]

[attribute_1]t[attribute_2]t [attribute_n-1]

• The test set does not have [attribute_name_n] (class label) n Example 1 (dt_test.txt):

Figure 3. An example of the first test set.

• Example 2 (dt_test1.txt):

Figure 4. An example of the second test set.

• Output file format

[attribute_name_1]t[attribute_name_2]t [attribute_name_n]

[attribute_1]t[attribute_2]t [attribute_n]

[attribute_1]t[attribute_2]t [attribute_n]

[attribute_1]t[attribute_2]t [attribute_n]

• Output file name: txt (for 1th dataset), dt_result1.txt (for 2^nd dataset) n You must print the following values:
  • [attribute_1] ~ [attribute_n-1]: given attribute values in the test set
  • [attribute_n]: a class label predicted by your model for the corresponding tuple n Please DO NOT CHANGE the order of the tuples in each test set.
  • You should print your outputs to match the order of correct answers.
• Please be sure to use t to identify your attributes.

5. Submission l Please submit the program files and the report to GitLab n Report

• File format must be *.pdf.
• Guideline Summary of your algorithm
• Detailed description of your codes (for each function)
• Instructions for compiling your source codes at TAs computer (e.g. screenshot) (Important!!) Any other specification of your implementation and testing

n Program and code

• An executable file

If you are in the following two cases, please submit alternative files (e.g., .py file, jar file, makefile)

1. You cannot meet the requirements (.exe file) of the programming assignment due to your computing environment (ex. Mac OS or Linux)
2. You are using python for implementing your program You MUST SUBMIT instructions for compiling your source codes. If TAs read your instructions but cannot compile your program, you will get a penalty. Please, write the instructions carefully.

• All source files

6. Testing program

• Please put the following files in a same directory: Testing program, your output files (dt_result.txt, dt_result1.txt), an attached answer file (dt_answer.txt, dt_answer1.txt)

• Execute the testing program with two arguments (answer file name and your output file name)

• Check your score for the input file

n the number of your correct prediction / the number of correct answers

• The test program was build with program mono. So, even if you are using mac or linux instead of window, you can run dt_test.exe using C# mono.