Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.
I practiced a similar question where logistic regression was highlighted as a go-to for binary outcomes, but I wonder if it handles high dimensions as well as others.
I think Naive Bayes could work too, especially since it assumes independence among features, but I’m not confident about its performance with high dimensions.
All of the options listed could work, but I'd probably lean towards naive Bayes. It's a simple and fast algorithm that can handle high-dimensional data, and it might be a good fit for this binary classification task.
Okay, I think I've got this. Logistic regression or support vector machines would both be good choices here. They can handle the high-dimensional data and binary outcome. I'll have to review the details of each to decide which one to use.
Hmm, I'm not sure which one to pick. Support vector machines and random forests both seem like good options for this type of problem. I'll have to think through the pros and cons of each.
This looks like a classic binary classification problem. I'd probably start with logistic regression since it's a simple and robust model that can handle high-dimensional data.
Nana
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