The purpose of this project is to build a decision tree by using the information gain, and then classify the test set using it.

• Execution file name: dt.exe
• Execute program with two arguments:
• training file name, test file name
• Example: dt.exe dt_train.txt dt_test.txt
• You should create the output file with predicted(classified) class label.

[attribute_name_1]\t[attribute_name_2]\t ... [attribute_name_n]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n]\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)
• [attribute_n]: a class label that the corresponding tuple belongs to

[attribute_name_1]\t[attribute_name_2]\t ... [attribute_name_n-1]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n-1]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n-1]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n-1]\n

• The test set does not have [attribute_name_n] (class label)

[attribute_name_1]\t[attribute_name_2]\t ... [attribute_name_n]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n]\n

[attribute_1]\t[attribute_2]\t ... [attribute_n]\n

• Output file name: dt_result.txt
• 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