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

To clone this repository install GIT on your computer and copy the link of the repository (find above at "Clone or Download") and enter in the command line:

git clone YOUR-LINK

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.

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.

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

pip install -r requirements.txt

The test cases can be run with:

python -m pytest -s

at the root directory of the assignment repository.

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!

Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8

Decision Tree Lecture Series

1. How it works
2. ID3 Algorithm
3. Which attribute to split on
4. Information Gain and Entropy

A cute (but good) introduction to Decision Trees

ID3-Algorith : Explanation

What is entropy in Data Science (very nice explanaton) Entropy as concept in Physics/Chemistry (only if you're interested)

Python: Recursion Explained Recursion example