In the workplace, you have been assigned to a new project. For this assignment you can chose what this project is for example it could be “indentifying poison and edible mushrooms”, “recognizing different groups of customers” or “recognising purchasing habits in supermarket” or “tracking different types of land use around rainforests” … you may use one of these examples or come up with your own.
At your next meeting with management, you have been asked to explain how ONE of the following algorithms works:Â
• ID3 (Information Gain Decision Tree) (classification)
• Naïve Bayes (Multinomial Naïve Bayes) (classification)
• K-means (clustering)
• DBSCAN (clustering)
• Apriori (Association Mining)
Your response must include:
1. A technical explanation, articulating how the algorithm works, showing how to work out the algorithm example by hand, using your own small example.
2. Comments on the strength and limitations of the algorithm.
3. Critically evaluate the algorithm for your given use case and compare with other similar algorithms and use-cases in research, the papers should be referenced, how you do this your choice.
4. Describe and reflect on the ethical considerations for using this algorithm, for example could the algorithm produce bias results; how would this happen?
