SIT720-Machine Learning Review Writing - IT Computer Science Assignment Help

Assignment Task

Task

Unit Learning Outcome (ULO):

ULO1 - Perform linear regression, classification using logistic regression and linear Support Vector Machines.

ULO2 - Perform non-linear classification using KNN and SVM with different kernels.

ULO3 - Perform non-linear classification using Decision trees and Random forests.

ULO4 - Perform model selection and compute relevant evaluation measure for a given problem.

ULO5 - Use concepts of machine learning algorithms to design solution and compare multiple solutions.

Background:

Energy production/consumption is the largest source of greenhouse gas emissions. Energy efficiency plays a crucial role in the transformation of future energy systems and cutting the rapid growth of global energy demand to able early decommissioning of fossil-fuel power plants and combat climate change. Electricity consumption in residential sectors accounts for more than 20% of total consumption, and thus energy-saving technology for residential buildings is of vital importance. Choosing the right time to consume the right amount of electricity will increase energy efficiency and reduce emissions.

Load monitoring (also known as load detection and load disaggregation) is a promising technique to provide detailed electricity consumption information and usage of individual appliances in residential buildings. An illustrative example of load monitoring for appliances, such as refrigerator, air conditioner, and stove, is shown in Figure 1. Take the oven as an example. When the oven is turned on, it is used for a period of time until being turned off. A more recent review of load monitoring can be found in the paper entitled “Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools-A review.”

Questions:

1. Load and explore the training dataset. Explain the steps that you have taken.

2. Analyse the importance of the features for predicting air conditioner status using two different approaches.                                                                                               

3. Based on the training data, create three supervised machine learning (ML) models except any ensemble approach for predicting air conditioner status.                                        

a. Report performance score using a suitable metric on the test data. Is it possible that the presented result is an underfitted or overfitted one? Justify.  
b. Justify different design decisions for each ML model used to answer this question.
c. Have you optimised any hyper-parameters for each ML model? What are they? Why have you done that? Explain. 
d. Finally, make a recommendation based on the reported results and justify it.

4. Given the same training and test data, build three ensemble models for predicting air conditioner status.

a. When do you want to use ensemble models over other ML models? 
b. What are the similarities or differences between these models?
c. Is there any preferable scenario for using any specific model among set of ensemble models?
d. Write a report comparing performances of models built in question 3 and 4. Report the best method based on model complexity and performance. 
e. Is it possible to build ensemble model using ML classifiers other than decision tree? If yes, then explain with an example. 

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