Q: 2
You want to create a model to figure out if a customer would be likely to repurchase a certain item.
The project owner doesn't want you to create anything too complicated, and you have a limited data
set to work with.
Which algorithm is the best choice given these constraints?
Options
Discussion
Makes sense to pick B here. Naive Bayes is lightweight and good for small datasets, while ensemble methods or neural nets would be overkill in this case I think.
Probably B, ensemble and neural nets are overkill with small data. D's not really for prediction tasks.
Super clear scenario, I'd pick B.
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