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
B. saw this type of question on a practice and it was Naive Bayes because it's easy and works fine with small datasets. Not 100% if accuracy is top but for constraints given I'd stick with B.
B. Saw this type of question pop up in some practice exams. Naive Bayes works great when you have limited data and want something simple, so that's why I'd choose B here. Open to other takes but pretty sure this is what they want.
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.
B , Naive Bayes is way simpler and fine with small data, A seems like a distractor here.
A , saw similar questions in some official practice tests suggesting ensemble models work with small datasets too.
Its A, not B. If you want a better performing model even with limited data, you can use ensemble techniques like bagging with small/simple base models. Sometimes Naive Bayes is too basic for real business accuracy needs. But I get why B is tempting here.
Option B guides and official exam questions always show Naive Bayes for small/simple data like this.
A or C. I think neural nets can work on small data too, and ensemble models might not be that complex if you keep them simple. Trap could be B because it's too basic for real business needs sometimes.
B tbh. Small dataset and need something easy, so Naive Bayes fits best here. Not totally sure but doesn’t seem like ensemble or neural nets make sense.
Option B. Ensemble models look tempting but they're overkill here and need more data. Naive Bayes fits the "simple + small dataset" angle, pretty sure that's why it's B.
Be respectful. No spam.
