COMP90049 - Introduction to Machine Learning - Project 2 - Solved

Naive Bayes and K-Nearest Neighbour for Predicting Stroke



Overview
In this project, you will implement Naive Bayes and K-Nearest Neighbour (K-NN) classifiers. You will explore inner workings and evaluate behavior on a data set of stroke prediction, and on this basis respond to some conceptual questions.

Implementation
The lectures and workshops contained several pointers for reading in data and implementing your Naive Bayes classifier. You are required to implement your Naive Bayes classifier from scratch. You may use Python libraries of your choice for implementing K-NN, evaluation metrics, procedures and data processing.



•    split_data(), where you split your data sets into a training set and a hold-out test set.

•    train(), where you build Naive Bayes and K-NN classifiers from the training data. You can create train your data as you answer the related question.

•    predict(), where you use a trained model to predict a class for the test data. You can also do prediction as you answer the related question.

•    evaluate(), where you will output the accuracy of your classifiers, or sufficient information so that it can be easily calculated by hand.

There is a sample iPython notebook S2020S2-proj1.ipynb that summarizes these, which you may use as a template.

You may alter the above prototypes to suit your needs; you may write other helper functions as you require. Depending on which answers you choose to respond, you may need to implement additional functions.

Packages
To provide the possibility of running your codes by markers, limit utilising the packages to the following packages in this project:

•    pandas to read, split and preprocess the data

•    sklearn to develop K-NN model and evaluate models

•    numpy to implement scientific computing

•    math to access to the mathematical functions

•    matplotlib to create plots and visualizations

Data

For this project, we have adapted the Stroke data that have been used for stroke prediction [1], available online at (https://data.mendeley.com/datasets/x8ygrw87jw/1):

Some critical information:

1.    File name: stroke_update.csv

2.    2740 instances

3.    10 attributes that include numeric and nominal attributes. The attributes avg_glucose_level, bmi and age are numeric. the rest of attributes are nominal.

4.    The file stroke_features.txt explains each attribute

5.    2 classes, corresponding to the stroke outcomes: {0: No stroke, 1: Having stroke}

Questions
You should respond to questions 1-3. In question 2 (b) you can choose between two options for smoothing and two options for Naive Bayes formulation. A response to a question should take about 100–200 words, and make reference to the data wherever possible.

Question 1

a    Explore the data and summarise different aspects of the data. Can you see any interesting characteristic in features, classes or categories? What is the main issue with the data? Considering the issue, how would the Naive Bayes classifier work on this data? Discuss your answer based on the Naive Bayes’ formulation [2]. (3 marks)

b    Is accuracy an appropriate metric to evaluate the models created for this data? Justify your answer. Explain which metric(s) would be more appropriate, and contrast their utility against accuracy. [no programming required] (2 marks)

Question 2

a    Explain the independence assumption underlying Naive Bayes. What are the advantages and disadvantages of this assumption? Elaborate your answers using the features of the provided data. [no programming required] (1 mark)

b    Implement the Naive Bayes classifier. You need to decide how you are going to apply Naive Bayes for nominal and numeric attributes. You can combine both Gaussian and Categorical Naive Bayes (option 1) or just using Categorical Naive Bayes (option 2). Explain your decision.

For Categorical Naive Bayes, you can choose either epsilon or Laplace smoothing for this calculation. Evaluate the classifier using accuracy and appropriate metric(s) on test data. Explain your observations on how the classifiers have performed based on the metric(s). Discuss the performance of the classifiers in comparison with the Zero-R baseline.

(4 marks) c Explain the difference between epsilon and Laplace smoothing. [no programming

required] (1 mark)

Question 3 a Implement the K-NN classifier, and find the optimal value for K. (1 mark)

b   Based on the obtained value for K in question 4 (a), evaluate the classifier using accuracy and chosen metric(s) on test data. Explain your observations on how the classifiers have performed based on the metric(s). Discuss the performance of the classifiers in comparison with the Zero- R baseline. (2 marks)

c    Compare the classifiers (Naive Bayes and K-NN) based on metrics’ results. Provide a

comparatory discussion on the results. [no programming required] (1 mark)

Data references
[1]                      T. Liu, W. Fan and C. Wu. A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artificial Intelligence in Medicine, volume. 101, No. 101723, 2019, DOI 10.1016/j.artmed.2019.101723

[2]                      M. A. Munoz, L. Villanova, D. Baatar and K. Smith-Miles. Instance spaces for machine learning classification. Machine Learning, volume. 107, pp. 109-147, 2018, DOI

10.1007/s10994-017-5629-5