report the performance(s) and interpretation(s) of the produced NN models on the training dataset

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Machine Learning 3. Apply one of the supervised machine learning (ML) algorithms from either Week 3 or Week 4 to the data prepared in Question 2 to predict the Australian unemployment rate from March 2018 to September 2020. (25 marks)

(a) Justify your choice over the other supervised machine learning algorithms.

(b) Justify the choice of the hyper-parameter(s) which is required to be specified in R to estimate the selected model.

(c) Report the performance(s) and interpretation(s) of the obtained ML model(s) on the training dataset. (d) Discuss the predictive performance of the model on the test dataset (March 2018 to December 2020). Neural Network

4. Apply a neural network (NN) to the data prepared in Question 2 to predict the Australian unemployment rate from March 2018 to September 2020. (35 marks)

(a) Describe the structure of the selected neural network model.

(b) Report the performance(s) and interpretation(s) of the produced NN models on the training dataset.

(c) Discuss the predictive performance of the model on the test dataset (March 2018 to December 2020).

(d) Vary the number of hidden layers in the model 4(a). Explore the impacts of the change on the prediction performance of the model.

(e) Vary the number of neurons in each layer in the model 4(a). Explore the impacts of the change on the prediction performance of the model. Comparison and Suggestion

5. Compare the chosen ML model in Question 3 with the NN model in Question 4, and then provide a recommended model. At a minimum, include (10 marks)

(a) Cross-validated accuracy

(b) Computational time to train models

(c) Interpretability 4

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