# 49929 – SIT720 Machine LearningAssessment 2: Problem solving

### Recent Question/Assignment

SIT720 Machine Learning
This document supplies detailed information on assessment tasks for this unit.
Key information
• Due: Wednesday 4 September 2019 by 11.30pm AEST
• Weighting: 25%
• Word count: Max 30 pages
Learning Outcomes
This assessment assesses the following Unit Learning Outcomes (ULO) and related Graduate Learning Outcomes (GLO):
Unit Learning Outcome (ULO) Graduate Learning Outcome (GLO)
ULO 2: Perform linear regression, and linear classification for two and more classes using logistic regression model. GLO 1: Discipline knowledge and capabilities
GLO 4: Critical thinking
GLO 5: Problem solving
ULO 5: Perform model assessment and selection for linear and logistic regression models. GLO 1: Discipline knowledge and capabilities GLO 4: Critical thinking
Purpose
Demonstrate your skills for applying regularized logistic regression to perform two-class and multi-class classification for realworld tasks. You also need to demonstrate your skill in recognizing under-fitting/overfitting situations.
Instructions
This is an individual assessment task of maximum 20 pages including all relevant material, graphs, images and tables. Students will be required to provide responses for series of problem situations related to their analysis techniques. They are also required to provide evidence through articulation of the scenario, application of programming skills, analysis techniques and provide a rationale for their response.
Part-1: Binary Classification
For this problem, we will use a subset of the Wisconsin Breast Cancer dataset. Note that this dataset has some information missing.
1.1 Data Munging (3 Marks)
Cleaning the data is essential when dealing with real world problems. Training and testing data is stored in -data/wisconsin_data- folder. You have to perform the following:
• Read the training and testing data. Print the number of features in the dataset. (0.5 marks)
• For the data label, print the total number of B’s and M’s in the training and testing data. Comment on the class distribution. Is it balanced or unbalanced? (0.5 marks)
• Print the number of features with missing entries (feature value is zero). (0.5 marks)
• Fill the missing entries. For filling any feature, you can use either mean or median value of the feature values from observed entries. Explain the reason behind your choice. (1.0 marks)
• Normalize the training and testing data. (0.5 marks)
1.2 Logistic Regression (5 Marks) Train logistic regression models with L1 regularization and L2 regularization using alpha = 0.1 and lambda = 0.1. Report accuracy, precision, recall, f1-score and print the confusion matrix.
1.3 Choosing the best hyper-parameter (7 Marks)
A- For L1 model, choose the best alpha value from the following set: {0.1,1,3,10,33,100,333,1000, 3333, 10000, 33333} based on parameter P. (2 Marks)
B- For L2 model, choose the best lambda value from the following set: {0.001, 0.003, 0.01, 0.03, 0.1,0.3,1,3,10,33} based on parameter P. (2 Marks)
[Hints: To choose the best hyperparameter (alpha/lambda) value, you have to do the following:
• For each value of hyperparameter, perform 10 random splits of training data into training (70%) and validation (30%) set.
• Use these 10 sets of data to find the average validation performance P.
• The best hyperparameter will be the one that gives maximum validation performance.
• Performance is defined as: P=’accuracy’ if fID=0, P=’f1-score’ if fID=1, P=’precision’ if fID=2. Calculate fID using modulus operation fID=SID % 3, where SID is your student ID. For example, if your student ID is 356288 then fID=(356288 % 3)=2 then use ‘precision’ for selecting the best value of alpha/lambda.]
C- Use the best alpha and lambda parameter to re-train your final L1 and L2 regularized model. Evaluate the prediction performance on the test data and report the following:
• Precision and Accuracy (1 Mark)
• The top 5 features selected in decreasing order of feature weights. (1 Mark)
• Confusion matrix (1 Mark)
Part-2 (Multiclass Classification):
For this experiment, we will use a small subset of MNIST dataset for handwritten digits. This dataset has no missing data. You will have to implement one-versus-rest scheme to perform multi-class classification using a binary classifier based on L1 regularized logistic regression.
2.1 Read and understand the data, create a default One-vs-Rest Classifier (3 Marks)
1- Use the data from the file reduced_mnist.csv in the data directory. Begin by reading the data. Print the following information: (1 Mark)
• Number of data points
• Total number of features
• Unique labels in the data
2- Split the data into 70% training data and 30% test data. Fit a One-vs-Rest Classifier (which uses Logistic regression classifier with alpha=1) on training data, and report accuracy, precision, recall on testing data. (2 Marks)
2.2 Choosing the best hyper-parameter (7 Marks)
1- Choose the best value of alpha from the set a={0.1, 1, 3, 10, 33, 100, 333, 1000, 3333, 10000, 33333} by observing average training and validation performance P. On a graph, plot both the average training performance (in red) and average validation performacne (in blue) w.r.t. each hyperparameter value. Comment on this graph by identifying regions of overfitting and underfitting. Print the best value of alpha hyperparameter. (2+1+1=5 Marks)
[Hints: To choose the best hyperparameter alpha value, you have to do the following:
• For each value of hyperparameter, perform 10 random splits of training data into training (70%) and validation (30%) set.
• Use these 10 sets of data to find the average training and validation performance P.
• The best hyperparameter shall be selected from the plot that shows both average training and validation performance against alpha value. While selecting the best alpha value you should consider overfitting and underfitting concepts.
• Performance is defined as: P=’accuracy’ if fID=0, P=’f1-score’ if fID=1, P=’precision’ if fID=2. Calculate fID using modulus operation fID=SID % 3, where SID is your student ID. For example, if your student ID is 356288 then fID=(356288 % 3)=2 then use ‘precision’ for selecting the best value of alpha.]
2- Use the best alpha and all training data to build the final model and then evaluate the prediction performance on test data and report the following: (1 Mark)
• The confusion matrix
• Precision, recall and accuracy for each class.
3- Discuss if there is any sign of underfitting or overfitting with appropriate reasoning. (1 Mark)
• Finding missing values
• Titanic Problem
• Numpy: Sorting and Searching
• Multiclass Classification
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 11.30pm, 4th of September.
• No marking on any submission after 5 days (24 hours X 5 days from 11.30pm 4th of September)
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 apply by email directly to Chandan Karmakar (karmakar@deakin.edu.au), 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.
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.
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
Part 1 Excellent Good Fair Unsatisfactory
* Read the training and testing data. Print the number of features in the dataset.
* For the data label, print the total number of B’s and M’s in the training and testing data. Comment on the class distribution. Is it balanced or unbalanced? * Print the number of features with missing entries. * Fill the missing entries. For filling any feature, you can use either mean or median value of the feature values from observed entries.
* Normalize the training and testing data. Successfully completed all five tasks. Successfully completed at least 3 tasks and satisfactorily tried other tasks. Successfully completed only two tasks. Failed to complete any task satisfactorily.
* Train logistic regression model with L1 regularization using alpha = 0.1.
* Train logistic regression model with L2 regularization using lambda = 0.1.
* Report accuracy, precision, recall, f1-score and print the confusion matrix. Successfully completed all three tasks.
Successfully completed any two of the three tasks. Successfully completed only one of the three tasks. Failed to complete any given task.
* For L1 model, choose the best alpha value from the provide set of values.
* For L2 model, choose the best lambda value from the provided set of values.
* Use the best alpha and lambda parameter to retrain your final L1 and L2 regularized model. Evaluate the prediction performance on the test data and report the following: – Precision and Accuracy
– The top 5 features selected in decreasing order of feature weights. – Confusion matrix Successfully completed all three tasks. Successfully completed any two of the three tasks. Successfully completed any one of the three tasks. Failed to complete any given task.
SIT720 Machine Learning