Project 1: Decision trees

(Keep an eye to this document on github. This will continue to be updated!)

About this homework

In this homework, you will be introduced to some basic machine learning concepts and be asked to implement and investigate decision trees. The homework will be divided into three parts.

Part A - Warm up to decision trees

In the first part of this assignment, you will be tasked to familiarize yourself with some machine learning concepts. You will also get some hands on experience with Jupyter notebooks and Python and implement a simple decision tree.

Follow the instructions in, and complete assignment_1a.ipynb.

Part B - Predicting survival of Titanic passengers with decision trees

In the second part of this assignment, you will apply the decision tree that you have implemented to a dataset we have prepared for you. Given a set of attributes of a Titanic passanger, you have to predict whether or not the given passenger survived!

Follow the instructions in, and complete assignment_1b.ipynb.

Part C - Information gain for decision trees

In the third part of the assignment, you will implement code to support the use of the information gain splitting criterion for decision tree learning.

Follow the instructions in, and complete assignment_1c.ipynb.

Files you will edit and submit

• assignment_1a.ipynb: Will be your edited copy of this notebook pertaining to part 1a of the assignment
• dumbClassifiers.py: This contains a handful of "warm up" classifiers to get you used to our classification framework.
• dt.py: Will be your simple implementation of a decision tree classifier
• assignment_1b.ipynb: Will be your edited copy of this notebook pertaining to part 1b of the assignment
• titanic-features.py: This contains some functions to help you apply a decision tree to the Titanic dataset
• assignment_1c.ipynb: Will be your edited copy of this notebook pertaining to part 1c of the assignment