Decision Tree Programming Assignment

Learning Objectives

After completing this assignment, students should be able to:

• Implement a decision tree for both classification and regression
• Describe how tree depth relates to underfitting and overfitting

Starter Code

Download the following files:

• decision_tree.py (Unfinished)
• draw_tree.py

Part 1: Warm Up

Complete the following nbgrader-based Jupyter notebook activity:

• tree_warmup.ipynb

Part 2: Implementation

Complete the stubbed-out decision tree classifier in decision_tree.py so that all public methods and attributes correspond to the provided docstring comments.

You may find it helpful to use draw_tree.py to confirming that your finished classifier is working correctly.

Part 3: Analysis

For this part of the assignment you will apply your decision tree implementation to the problem of predicting the number of bike rentals on the basis of time of day, time of year, and weather conditions.

For this part of the assignment you should submit a Jupyter notebook that satisfies the following requirements:

• It should illustrate hyper-parameter tuning using cross validation.
• It should illustrate model evaluation by determining the classification error on the test set.
• It should include a 4x4 confusion matrix illustrating classification results on the test data.
• It should include a discussion of which attributes were used by your decision tree.

Each step of the notebook must be accompanied by text explaining the point of the provided Python code and discussing the results.

Data Set

The following files contain the training and test data you must use for your analysis:

• X_train.npy
• y_train.npy
• X_test.npy
• y_test.npy

The class labels are integers in the range 0-3 where

• 0: 0-190 rentals
• 1: 191-504 rentals
• 2: 505-1065 rentals
• 3: more than 1065 rentals

The data set includes the following 12 attributes:

• Hour (0-23)
• Temperature(C)
• Humidity(%)
• Wind speed (m/s)
• Visibility (10m)
• Dew point temperature(C)
• Solar Radiation (MJ/m2)
• Rainfall(mm)
• Snowfall(cm)
• Seasons, “Winter”: 0, “Spring”: 1, “Summer”: 2, “Autumn”: 3
• Holiday, No: 0, Yes: 1
• Functioning Day, No: 0, Yes: 1

This data is a processed version of the Seoul Bike Sharing Demand Data Set.

Partners

Parts 1 and 2 must be completed individually.

Part 3 may be completed individually or in pairs. My expectation for pairs is that both members are actively involved, and take full responsibility for all aspects of the project. In other words, I expect that you are sitting down together to work, not that you are splitting up tasks to be completed separately.

If you intend to work with a partner, you must inform me no later than Tuesday 2/6.

Grading

Grades will be calculated according to the following distribution.

• Readability/Style 10%

  Your code should follow PEP8 conventions. It should be well documented and well organized.

• Part 1 Submission 6%

• Part 2 Reference Tests 54%

  • decision_tree_classifier_tests.py
  • decision_tree_regressor_tests.py

• Part 2 Efficiency 10%

  Our main concern with this assignment is clarity and correctness. That said, your implementation must be efficient enough execute the provided testing code in no more than a second or two. This means you should avoid Python loops where possible.

• Part 3 Submission 20%