60012 Introduction to Machine Learning: Coursework 1 (Decision Trees)

Introduction

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

Data

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).

Codes

• classification.py

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

• improvement.py

  • Contains the skeleton code for the train_and_predict() function (Task 4.2). Complete this function as an interface to your new/improved decision tree classifier.

• example_main.py

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

Instructions

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