This document supplies detailed information on Assessment

Question:

Background:

Assessment 5: Machine Learning Project

This document supplies detailed information on Assessment Task 5 for this unit.

Key information

• Due: Thursday 1 October 2020 11:30 pm (AEST)
• Weighting: 35%

Learning Outcomes

This assessment assesses the following Unit Learning Outcomes (ULO) and related Graduate Learning Outcomes (GLO):

Purpose

This assessment is an extensive machine learning project. Students will be given a specific data set for analysis and will be required to develop and compare various classification techniques. Each student must demonstrate skills acquired in data representation, classification and evaluation.

Instructions

Students should insert Python code or text responses into the cell followed by the question into the supplied ipynb (Jupyter Notebook) file. For answers regarding discussion or explanation, maximum five sentences are suggested. Rename this Jupyter notebook file appending your student ID. For example, for student ID 1234, the submitted file name should be A5_1234.ipynb. Insert your student ID and name in the appropriate cell inside that file.

Data filename: chronic_kidney_disease.csv

Attribute Information:

There are 24 features + class=25 attributes 1.Age(numerical): age in years

2.Blood Pressure(numerical): bp in mm/Hg

3.Specific Gravity(nominal): sg – (1.005,1.010,1.015,1.020,1.025) 4.Albumin(nominal): al – (0,1,2,3,4,5)

5.Sugar(nominal): su – (0,1,2,3,4,5)

6.Red Blood Cells(nominal): rbc – (normal,abnormal) 7.Pus Cell (nominal): pc – (normal,abnormal)

8.Pus Cell clumps(nominal): pcc – (present,notpresent) 9.Bacteria(nominal): ba – (present,notpresent) 10.Blood Glucose Random(numerical): bgr in mgs/dl 11.Blood Urea(numerical): bu in mgs/dl

12.Serum Creatinine(numerical): sc in mgs/dl 13.Sodium(numerical): sod in mEq/L 14.Potassium(numerical): pot in mEq/L 15.Hemoglobin(numerical): hemo in gms 16.Packed Cell Volume(numerical)

17.White Blood Cell Count(numerical): wc in cells/cumm 18.Red Blood Cell Count(numerical): rc in millions/cmm 19.Hypertension(nominal): htn – (yes,no)

20.Diabetes Mellitus(nominal): dm – (yes,no) 21.Coronary Artery Disease(nominal): cad – (yes,no) 22.Appetite(nominal): appet – (good,poor)

23.Pedal Edema(nominal): pe – (yes,no) 24.Anemia(nominal): ane – (yes,no) 25.Class (nominal): class – (ckd, notckd)

Part-1: Short questions: (6 marks)

1. For the above figure, what value of k in KNN method will give the best accuracy for leave-one-out cross- validation. Report accuracy and k value. (3 marks)
2. In classification, overfitting and underfitting is a big Does it happen in Random Forest or not? Why? (3 marks)

Part-2: (24 marks = 4 methods x 6):

Using the following four supervised machine learning methods, answer questions(A-D).

1. Support Vector Machine
2. K-Nearest Neighbour
3. Decision Tree, and
4. Random Forest

1. Build optimised classification model to predict the chronic kidney disease from the dataset. (1 marks)
2. For each optimised model, answer the followings –
   • which hyperparameters were optimised? [Hint: For SVM, kernel can be considered as one of the hyperparameters.]
   • what set or range of values were used for each hyperparameter?
   • which metric was used to measure the performance?
   • justify your design
3. Plot the prediction performance against hyperparameter values to visualise the optimisation process and mark the optimal value. (1 marks)

1. Evaluate the model (obtained from A) performance on the test set. Report the confusion matrix, F1-score and accuracy. (1 marks)

Part-3: Discussion (5 marks)

Based on the results obtained in Part-2, which classification method showed the best performance and why? Do you have any suggestions to further improve the model performances? (5 marks)

Submission details

Deakin University has a strict standard on plagiarism as a part of Academic Integrity. To avoid any issues with plagiarism, students are strongly encouraged to run the similarity check with the Turnitin system, which is available through Unistart. A Similarity score MUST NOT exceed 39% in any case. Late submission penalty is 5% per each 24 hours from- Thursday 1 October 2020 11:30 pm (AEST). No marking on any submission after 5 days (24 hours X 5 days from- Thursday 1 October 2020 11:30 pm (AEST)).

Extension requests

Requests for extensions should be made to Unit/Campus Chairs well in advance of the assessment due date. If you wish to seek an extension for an assignment, you will need to submit a request using the “Extension Request” link of the “Assessment” menu in the unit site, as soon as you become aware that you will have difficulty in meeting the scheduled deadline, but at least 3 days before the due date. When you make your request, you must include appropriate documentation (medical certificate, death notice) and a copy of your draft assignment. Conditions under which an extension will normally be approved include:

Medical To cover medical conditions of a serious nature, e.g. hospitalisation, serious injury or chronic illness. Note: Temporary minor ailments such as headaches, colds and minor gastric upsets are not serious medical conditions and are unlikely to be accepted. However, serious cases of these may be considered.

Compassionate e.g. death of close family member, significant family and relationship problems.

Hardship/Trauma e.g. sudden loss or gain of employment, severe disruption to domestic arrangements, victim of crime. Note: Misreading the timetable, exam anxiety or returning home will not be accepted as grounds for consideration.

Special consideration

You may be eligible for special consideration if circumstances beyond your control prevent you from undertaking or completing an assessment task at the scheduled time. See the following link for advice on the application process: http://www.deakin.edu.au/students/studying/assessment-and-results/special-consideration.

Assessment feedback

The results with comments will be released within 15 business days from the due date.

Referencing

You must correctly use the Harvard method in this assessment. See the Deakin referencing guide.

Academic integrity, plagiarism and collusion

Plagiarism and collusion constitute extremely serious breaches of academic integrity. They are forms of cheating, and severe penalties are associated with them, including cancellation of marks for a specific

assignment, for a specific unit or even exclusion from the course. If you are ever in doubt about how to properly use and cite a source of information refer to the referencing site above.

Plagiarism occurs when a student passes off as the student’s own work, or copies without acknowledgement as to its authorship, the work of any other person or resubmits their own work from a previous assessment task.

Collusion occurs when a student obtains the agreement of another person for a fraudulent purpose, with the intent of obtaining an advantage in submitting an assignment or other work.

Work submitted may be reproduced and/or communicated by the university for the purpose of assuring academic integrity of submissions: https://www.deakin.edu.au/students/study-support/referencing/academic-integrity.