This repository contains the skeleton code and dataset files that you need in order to complete the coursework.

The data/ directory contains the datasets you need for the coursework.

The primary datasets are:

• train_full.txt
• train_sub.txt
• train_noisy.txt
• validation.txt

Some simpler datasets that you may use to help you with implementation or debugging:

• toy.txt
• simple1.txt
• simple2.txt

The official test set is test.txt. Please use this dataset sparingly and purely to report the results of evaluation. Do not use this to optimise your classifier (use validation.txt for this instead).

• classification.py

  • Contains the skeleton code for the DecisionTreeClassifier class. Your task is to implement the train() and predict() methods.

• eval.py

  • Contains the skeleton code for the Evaluator class. Your task is to implement the confusion_matrix(), accuracy(), precision(), recall(), and f1_score() methods.

• example_main.py

  • Contains an example of how the evaluation script on LabTS might use the classes and invoke the methods defined in classification.py and eval.py.

• We're not using exception handling because that's too much effort.