SIT720 - IT - Machine Learning - Understanding the Data - K Nearest Neighbour Classification - Assessment Answer

SIT720: Machine Learning Understanding the Data  K-Nearest Neighbour Classification  Assessment Answer

Assessment Task:

Part 1: Understanding the data 
Answer the following questions briefly, after reading the paper
• What is the objective of the data collection process? 
• What human activity types does this dataset have? How many subjects/people have performed these activities? 
 • How many instances are available in the training and test sets? How many features are used to represent each instance? Summarize the type of features extracted in 2 3 sentences. 
• Describe briefly what machine learning model is used in this paper for activity recognition and how is it trained. How much is the maximum accuracy achieved? 

Part 2: K-Nearest Neighbour Classification 
Build a K-Nearest Neighbour classifier for this data.
• Let K take values from 1 to 50. Show a plot of cross-validation accuracy with respect to K. 
• Choose the best value of K based on model performance P. 

• Using the best K value, evaluate the model performance on the supplied test set. Report the confusion matrix, multi-class averaged F1-score and accuracy.

Part 3: Multiclass Logistic Regression with Elastic Net 
Build an elastic-net regularized logistic regression classifier for this data.

• Elastic-net regularizer takes in 2 parameters: alpha and l1-ratio. Use the following values for alpha: 1e-4,3e-4,1e-
3,3e-3, 1e-2,3e-2. Use the following values for l1-ratio: 0,0.15,0.5,0.7,1. Choose the best values of alpha and l1-ratio based on model performance P. 
• Draw a surface plot of F1-score with respect to alpha and l1-ratio values. 
• Use the best value of alpha and l1-ratio to re-train the model on the training set and use it to predict the labels of the test set. Report the confusion matrix, multi-class averaged F1-score and accuracy. 

Part 4: Support Vector Machine (RBF Kernel) 
Build an SVM (with RBF Kernel) classifier for this data.
• SVM with RBF takes 2 parameters: gamma (length scale of the RBF kernel) and C (the cost parameter). Use the following values for gamma: 1e-3, 1e-4. Use the following values for C: 1, 10, 100, 1000. Choose the best values of gamma and C based on model performance P. 
• Draw a surface plot of F1-score with respect to gamma and C. Describe the graph. 
• Use the best value of gamma and C to re-train the model on the training set and use it to predict the labels of the test set. Report the confusion matrix, multi-class averaged F1-score and accuracy. 

Part 4: Support Vector Machine (RBF Kernel)
Build an SVM (with RBF Kernel) classifier for this data.
• SVM with RBF takes 2 parameters: gamma (length scale of the RBF kernel) and C (the cost parameter). Use the following values for gamma: 1e-3, 1e-4. Use the following values for C: 1, 10, 100, 1000. Choose the best values of gamma and C based on model performance P. 
• Draw a surface plot of F1-score with respect to gamma and C. Describe the graph. 
• Use the best value of gamma and C to re-train the model on the training set and use it to predict the labels of the test set. Report the confusion matrix, multi-class averaged F1-score and accuracy. 

Part 6: Discussion 
• Write a brief discussion about which classification method achieved the best performance and your thoughts on the reason behind this. 
• Which method performed the worst and why? 
• Do you have any suggestions to further improve model performances? 

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