MiniProject 1: Getting Started with Machine Learning

MiniProject 1: Getting Started with Machine Learning
COMP 551
Please read this entire document before beginning the assignment.
Preamble
• We recommend to use Overleaf for writing your report and Google colab for coding and running the experiments. The latter also gives access to the required computational resources. Both platforms enable remote
collaborations.
• You should use Python for this and the following mini-projects. You are free to use libraries with general
utilities, such as matplotlib, numpy and scipy for Python, unless stated otherwise in the description of the task.
In particular, in most cases you should implement the models and evaluation functions yourself, which means
you should not use pre-existing implementations of the algorithms or functions as found in SciKit learn, and
other packages. The description will specify this in a per case basis.
Background
In this miniproject you will implement two classification techniques—K-Nearest Neighbour and Decision Trees—
and compare these two algorithms on two distinct health datasets. The goal is to get started with programming for
Machine Learning, how to properly store the data, run the experiments, and compare different methods. You will also
gain experience implementing these algorithms from scratch and get hands-on experience comparing performance of
different models.
Task 1: Acquire, preprocess, and analyze the data
Your first task is to acquire the data, analyze it, and clean it (if necessary). We will use two fixed datasets in this
project, outlined below.
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• Dataset 1: breast cancer wisconsin.csv (Breast Cancer dataset):
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
• Dataset 2: hepatitis.csv (Hepatitis dataset):
http://archive.ics.uci.edu/ml/datasets/Hepatitis
The essential subtasks for this part of the project are:
1. Load the datasets into NumPy or Pandas objects in Python.
2. Clean the data. Are there any missing or malformed features? Are there are other data oddities that need to be
dealt with? You should remove any examples with missing or malformed features and note this in your
report.
If you choose to play with Pandas dataframes, a handy line of code that might be helpful is df[˜df.eq(’?’).any(1)],
where df is the dataframe, and ’?’ represents a missing value in the datasets. This is a straightforward way to
handle this issue by simply eliminating rows with missing values. You are welcome to explore other possible
ways!
3. Compute basic statistics on the data to understand it better. E.g., what are the distributions of the positive vs.
negative classes, what are the distributions of some of the numerical features?
Task 2: Implement the models
You are free to implement these models as you see fit, but you should follow the equations that are presented in
the lecture slides, and you must implement the models from scratch (i.e., you CANNOT use SciKit Learn or any
other pre-existing implementations of these methods). However, you are free to use relevant code given in the course
website.
In particular, your two main tasks in the part are to:
1. Implement K - Nearest Neighbour .
2. Implement Decision Tree with appropriate cost function.
You are free to implement these models in any way you want, but you must use Python and you must implement
the models from scratch (i.e., you cannot use SciKit Learn or similar libraries). Using the NumPy or Pandas package, however, is allowed and encouraged. Regarding the implementation, we recommend the following
approach (but again, you are free to do what you want):
• Implement both models as Python classes. You should use the constructor for the class to initialize the model
parameters as attributes, as well as to define other important properties of the model.
• Each of your models classes should have (at least) two functions:
– Define a fit function, which takes the training data (i.e., X and Y)—as well as other hyperparameters (e.g.,
K value in KNN and maximum tree depth in Decision Tree)—as input. This function should train your model
by modifying the model parameters.
– Define a predict function, which takes a set of input points (i.e., X) as input and outputs predictions (i.e.,
yˆ) for these points.
• In addition to the model classes, you should also define a functions evaluate_acc to evaluate the model accuracy.
This function should take the true labels (i.e., y), and target labels (i.e., yˆ) as input, and it should output the accuracy
score.
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Task 3: Run experiments
The goal of this project is to have you compare different features and models.
Split each dataset into training and test sets. Use test set to estimate performance in all of the experiments after
training the model with training set. Evaluate the performance using accuracy. You are welcome to perform any
experiments and analyses you see fit (e.g., to compare different features), but at a minimum you must complete the
following experiments in the order stated below:
1. Compare the accuracy of KNN and Decision Tree algorithm on the two datasets.
2. Test different K values and see how it affects the training data accuracy and test data accuracy.
3. Similarly, check how maximum tree depth can affect the performance of of Decision Tree on the provided
datasets. Describe your findings.
4. Try out different distance/cost functions for both models. Describe your findings.
5. Present a plot of the decision boundary for each model. Describe the key features in short.
Note: The above experiments are the minimum requirements that you must complete; however, this project is
open-ended. For example, you might investigate different stopping criteria for Decision Tree or different features
that you select for the training process. We would also love to see possible ways to improve model performance. You
do not need to do all of these things, but you should demonstrate creativity, rigour, and an understanding of the course
material in how you run your chosen experiments and how you report on them in your write-up.
Deliverables
You must submit two separate files to MyCourses (using the exact filenames and file types outlined below):
1. code.zip: Your data processing, classification and evaluation code (as some combination of .py and .ipynb files).
2. writeup.pdf: Your (max 5-page) project write-up as a pdf (details below).
Project write-up
Your team must submit a project write-up that is a maximum of five pages (single-spaced, 11pt font or larger; minimum
0.5 inch margins, an extra page for references/bibliographical content can be used). We highly recommend that students use LaTeX to complete their write-ups. This first mini-project report has relatively strict requirements, but
as the course progresses your project write-ups will become more and more open-ended. You have some flexibility in how you report your results, but you must adhere to the following structure and minimum requirements:
Abstract (100-250 words) Summarize the project task and your most important findings. For example, include
sentences like “In this project we investigated the performance of two machine learning models on two benchmark datasets”, “We found that the Decision Tree approach achieved worse/better accuracy than K - Nearest Neighbour.”
Introduction (5+ sentences) Summarize the project task, the two datasets, and your most important findings. This
should be similar to the abstract but more detailed. You should include background information and citations to
relevant work (e.g., other papers analyzing these datasets).
Datasets (5+ sentences) Very briefly describe the datasets and how you processed them. Present the exploratory
analysis you have done to understand the data, e.g. class distribution. Highlight any possible ethical concerns that
might arise when working these kinds of datasets.
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Results (7+ sentences, possibly with figures or tables) Describe the results of all the experiments mentioned in
Task 3 (at a minimum) as well as any other interesting results you find (Note: demonstrating figures or tables would
be an ideal way to report these results).
Discussion and Conclusion (5+ sentences) Summarize the key takeaways from the project and possibly directions
for future investigation.
Statement of Contributions (1-3 sentences) State the breakdown of the workload across the team members.
Evaluation
The mini-project is out of 100 points, and the evaluation breakdown is as follows:
• Completeness (20 points)
– Did you submit all the materials?
– Did you run all the required experiments?
– Did you follow the guidelines for the project write-up?
• Correctness (40 points)
– Are your models implemented correctly?
– Are your reported accuracies close to our solution?
– Do you observe the correct trends in the experiments (e.g., how the accuracy changes as the maximum
depth of Decision Tree increases)?
– Do you observe the correct impact of different distance/cost functions on model performance?
– Do you find notable features of the decision boundaries?
• Writing quality (25 points)
– Is your report clear and free of grammatical errors and typos?
– Did you go beyond the bare minimum requirements for the write-up (e.g., by including a discussion of
related work in the introduction)?
– Do you effectively present numerical results (e.g., via tables or figures)?
• Originality / creativity (15 points)
– Did you go beyond the bare minimum requirements for the experiments?
– Note: Simply adding in a random new experiment will not guarantee a high grade on this section! You
should be thoughtful and organized in your report. That is, the distinctive ideas that you came up with
should blend in your whole story. For instance, explaining the triggers behind them would be a great
starting point.
Final remarks
You are expected to display initiative, creativity, scientific rigour, critical thinking, and good communication skills.
You don’t need to restrict yourself to the requirements listed above - feel free to go beyond, and explore further.
You can discuss methods and technical issues with members of other teams, but you cannot share any code or data
with other teams.
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Congratulations on completing your first course project! You are now capable of making simple diagnosis of
disease based on patients’ health data. This is one of the most significant applications of classification algorithms. As the class goes on, we will see more machine learning models and their interesting applications in real
life.
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