机器学习代写 | SIT720 Machine Learning Assessment Task 4: Problem solving task
1. What is an ensemble classifier? Name some of the popular ensemble methods (at least three) and which
one you prefer and why? (2 marks)
2. Let’s assume we have a noisy dataset. You want to build a classifier model. Which classifier is appropriate
for your dataset and why? (2 marks)
In the modern world, customer details are very important to suggest any product for buying. Gender, age and
education have impact on level of consumption of different products. So, it is essential for businesses to
analyse their customer details to better understand consumer behaviour and their impact on various products.
Dataset filename: Customer relationship marketing (CRM).csv
Dataset description: This dataset includes data on customer details and their response to buy any products.
The data contains 20 attributes and 9134 records.
Features and labels: The attribute names are listed below.
II. Customer Lifetime Value
VI. Effective To Date
X. Location Code
XI. Marital Status
XII. Monthly Premium Auto
XIII. Months Since Last Claim
XIV. Number of Open Complaints
XV. Number of Policies * Policy
XVI. Renew Offer Type
XVII. Sales Channel
XVIII. Total Claim Amount
XIX. Vehicle Class
4. Analyse the importance of the features for predicting customer response using two different approaches.
Explain the similarity/difference between outcomes. (5 marks)
5. Create three supervised machine learning (ML) models except any ensemble approach for predicting
customer response. (10 Marks)
a. Report performance score using a suitable metric. Is it possible that the presented result is an
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.
6. Build three ensemble models for predicting customer response. (6 Marks)
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 5 and 6. 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.