# Assignment 1: Decision Trees

• Posting date: Jan 22th 2020.
• Due date: Jan 29th 2020, 11:59PM MST.
• Github Classroom link: Assignment 1

## Assignment description

In this assignment, we will implement a full ML classifier based on decision trees. The datasets we will use to train and evaluate your classifier are:

Both datasets come from the UCI ML repository.

You will not need to download the data from the repository: instead, you will use the data provided in the repository that GitHub will create automatically for you when you click on the GitHub Classroom link above.

You will submit Python 3 code that should work with Python 3.4, out of the box (I myself run Python 3.6.3, for what’s worth). This means unless otherwise indicated, you’re not supposed (or expected) to use libraries such as numpy and scipy.

## Assignment Problems

1. Implement the basic decision tree procedure as described in the textbook.

You will implement DecisionTreeTrain as described in page 13 of CIML (in the skeleton code we have provided, the name of the procedure is simply train).

2. Implement the information gain criterion as described in Quinlan 19861.

Provide a separate version of DecisionTreeTrain that uses the information gain criterion described in the paper (this is colloquially known as the ID3 criterion, for the system that first implemented it)

3. Implement tree depth control as a means of controlling model complexity.

The Python procedure train you will implement takes a parameter remaining_depth. Use this parameter to stop further refinements of the tree.

4. Write a short report in Markdown (or, at best, plaintext) named report.md on the training and test accuracies you obtain with both datasets, as you vary the complexity of your model. Is there a qualitative difference between the two of them? Explain.

Each of those problems above is worth the same amount of credit.

In order for you to receive full credit for this (and future assignments), I will need to be able to run the code you submit. You’re encouraged to split your code in multiple files (or however else you see fit) for organization, reusability, and clarity. But your code has to work under the following interface:

## Spec

In order to evaluate your homework, I will run it by typing the following, on my shell:

$python3 decision-tree-basic.py <dataset.pickle> <tree-depth>$ python3 decision-tree-id3.py <dataset.pickle> <tree-depth>


Your code should produce output that looks like this:

$python3 decision_tree_basic.py primary-tumor.pickle 3 Training... Training complete. Evaluating... Evaluation complete: Training: 73/169: 43.20% Validation: 37/85: 43.53% Testing: 33/85: 38.82%  ## Data, source code In case you want to access the files from the repository directly from the web, they’re also available here. Make sure you can run describe-data.py sooner rather than later! You should get these outputs for the two datasets. $ python3 describe-data.py agaricus-lepiota.pickle
Dataset description:
Training set:   4062 observations
Label distribution:
Label e: 2108
Label p: 1954
Validation set: 2031 observations
Label distribution:
Label e: 1049
Label p: 982
Testing set:    2031 observations
Label distribution:
Label e: 1051
Label p: 980

\$ python3 describe-data.py primary-tumor.pickle
Dataset description:
Training set:   169 observations
Label distribution:
Label 1: 36
Label 2: 13
Label 3: 7
Label 4: 4
Label 5: 25
Label 7: 6
Label 8: 3
Label 11: 14
Label 12: 9
Label 13: 4
Label 14: 14
Label 16: 1
Label 17: 4
Label 18: 14
Label 19: 3
Label 20: 1
Label 21: 1
Label 22: 10
Validation set: 85 observations
Label distribution:
Label 1: 24
Label 2: 6
Label 3: 2
Label 4: 4
Label 5: 3
Label 7: 6
Label 8: 1
Label 10: 1
Label 11: 5
Label 12: 3
Label 13: 2
Label 14: 8
Label 15: 1
Label 17: 2
Label 18: 8
Label 19: 2
Label 20: 1
Label 22: 6
Testing set:    85 observations
Label distribution:
Label 1: 24
Label 2: 1
Label 4: 6
Label 5: 11
Label 6: 1
Label 7: 2
Label 8: 2
Label 10: 1
Label 11: 9
Label 12: 4
Label 13: 1
Label 14: 2
Label 15: 1
Label 17: 4
Label 18: 7
Label 19: 1
Label 22: 8


## Other

One of the datasets we’re using was originally collected by the Audobon Society Field Guide, and comes the following warning: ‘The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like “leaflets three, let it be” for Poisonous Oak and Ivy.’ Please don’t use this dataset to make your foraging decisions!

1. Quinlan, J. Ross. “Induction of decision trees”. Machine learning 1, no. 1 (1986): 81-106.