sesameintro/hw1-decision-trees

Decision trees

HW1: Decision trees for EECS 349 @ NU

IMPORTANT: PUT YOUR NETID IN THE FILE netid in the root directory of the assignment. This is used to put the autograder output into Canvas. Please don't put someone else's netid here, we will check.

In this assignment, you will:

• Understand and implement evaluation measures for machine learning algorithms
• Implement information gain and entropy measures
• Implement a decision tree with the ID3 algorithm
• Implement a prior probability classifier
• Compare and contrast machine learning approaches on different datasets
• Write up your results in a clear concise report

Clone this repository

To clone this repository run the following command:

git clone https://github.com/nucs349/hw1-decision-trees-[your_username]

[your_username] is replaced in the above link by your Github username. Alternatively, just look at the link in your address bar if you're viewing this README in your submission repository in a browser. Once cloned, cd into the cloned repository. Every assignment has some files that you edit to complete it.

Files you edit

See problems.md for what files you will edit.

Do not edit anything in the tests directory. Files can be added to tests but files that exist already cannot be edited. Modifications to tests will be checked for.

Environment setup

Make a conda environment for this assignment, and then run:

pip install -r requirements.txt

Running the test cases

The test cases can be run with:

python -m pytest -s

at the root directory of the assignment repository.

Questions? Problems? Issues?

Simply open an issue on the starter code repository for this assignment here. Someone from the teaching staff will get back to you through there!