CA Data Classification algorithm

CA Assignment 1 Data Classification Implementing Perceptron algorithm

Assessment Information

Assignment Number 1 (of 2)

Weighting 15%

Assignment Circulated 10 Feb 2023

Deadline 3 March 2023 at 17:00Submission Mode Electronic via CanvasPurpose of assessment The purpose of this assignment is todemonstrate: (1) the understanding of thePerceptron algorithm; (2) the ability toimplement the Perceptron algorithm for binary classification; (3) the ability to evaluate a classification algorithm; (4) the ability to turna binary classification algorithm to a multi-class classification algorithm using the 1-vs-rest approach; (4) the ability toincorporate regularisation into a classificationalgorithm.Learning outcome assessed (1) A critical awareness of current problemsand research issues in data mining. (3) Theability to consistently apply knowledge concerning current data mining research issues in an original manner and produce work whichis at the forefront of current developments inthe sub-discipline of data mining.

1Objectives

This assignment requires you to implement the Perceptron algorithm using the Python programming language.

Assignment description

Download the CA1data.zip file. Inside, you will find two files: train.data and test.data, corresponding respectively to the train and test data to be used in this assignment. Each line in thefile represents a different train/test instance. The first four values (separated by commas) arefeature values for four features. The last element is the class label (class-1, class-2 or class-3).

Questions/Tasks

1. (15 marks) Explain the Perceptron algorithm (both the training and the test procedures)for the binary classification case. Provide the pseudo code of the algorithm. It should bethe most basic version of the Perceptron algorithm, i.e. the one that was discussed in thelectures.

2. (30 marks) Implement a binary perceptron. The implementation should be consistent with the pseudo code in the answer to Question 1.

3. (15 marks) Use the binary perceptron to train classifiers to discriminate between

• class 1 and class 2,
• class 2 and class 3, and
• class 1 and class 3.Report the train and test classification accuracies for each of the three classifiers aftertraining for 20 iterations. Which pair of classes is most difficult to separate?

4. (30 marks) Explain in your own words what the 1-vs-rest approach consist of. Extend thebinary perceptron that you implemented in part 3 above to perform multi-class classificationusing the 1-vs-rest approach. Report the train and test classification accuracies for the

multi-class classifier after training for 20 iterations.

5. (10 marks) Add an ` 2 regularisation term to your multi-class classifier implemented inpart 4. Set the regularisation coefficient to 0.01, 0.1, 1.0, 10.0, 100.0 and compare the train

and test classification accuracies. What can you conclude from the results?

Submission Instructions

Submit via Canvas the following two files (please do NOT zip files into an archive)

1. the source code for all your programs (do not provide ipython/jupyter/colab note

books, instead submit standalone code in a single .py file), and

2. a PDF file (report) of no more than 3 pages providing the answers to the questions.It is extremely important that you provide the two files described above and not just the sourcecode!

2Important notes

(read carefully and double check compliance before submission)

1. No credit will be given for implementing any other type of classification algorithm or usingan existing library for classification instead of implementing it by yourself. However, youare allowed to use

• numpy library for accessing data structures such as numpy.array;
• random module; and
• pandas.read_csv, csv.reader, or similar modules only for reading data from the files.

However, it is not a requirement of the assignment to use any of those modules.

2. Your program

• should run and produce all results for Questions 3, 4, and 5 in one click withoutrequiring any changes to the code;

• should output only the required data in a clearly structured way; it should NOToutput any intermediate steps;

• should assume that the input files are named ‘test.data’ and ‘train.data’, and arelocated in the same folder as the program; in particular, it should NOT use absolutepaths.

3. Programs that do not run will result in a mark of zero!
4. Your code should be as clear as possible and should contain only the functionality neededto answer the questions. Provide as much comments as needed to make sure that the logicof the code is clear enough to a marker. Marks may be deducted if the code is obscure,mplements unnecessary functionality, or is overly complicated.

5. You are allowed to shuffle the data. If you use module random to shuffle the data, usea fixed seed value so that your program always produces the same output. This outputshould be exactly the one that you provide in the PDF report.

6. Your answers in the PDF report should be succinct, but complete and clear. The clarityand presentation of the report will be assessed.

7. Your submission should be your own work. Do not copy or share! Make sure that youclearly understand the severity of penalties for academic misconduct (https://www.liverpool.ac.uk/media/livacuk/tqsd/code-of-practice-on-assessment/appendix_L_cop_assess.pdf).