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Databricks Exam Databricks Certified Professional Data Scientist Topic 1 Question 87 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 87
Topic #: 1
[All Databricks Certified Professional Data Scientist Questions]

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.

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Suggested Answer: E

Contribute your Thoughts:

Nana
55 minutes ago
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.
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Lauran
6 days ago
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.
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Glen
11 days ago
I remember we discussed support vector machines being effective for high-dimensional data, but I'm not entirely sure if it's the best choice here.
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Keneth
17 days ago
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.
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Samira
22 days ago
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.
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Delpha
28 days ago
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.
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Nikita
1 month ago
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.
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Cherelle
2 months ago
Naive Bayes, really? I thought that went out of style in the 90s. But hey, maybe it's making a comeback - kind of like scrunchies!
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Isabelle
2 months ago
All of the above, huh? I guess the exam writers are feeling generous today. I'll go with that just to be safe!
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Thora
3 months ago
I think E) All of the above could be used, depending on the data.
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Laurel
3 months ago
I disagree, I believe A) Support vector machines would work better.
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Ashlyn
3 months ago
I think C) Logistic regression would be the best fit.
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Susy
3 months ago
Hmm, I'm torn between support vector machines and logistic regression. Both are solid choices, but I think I'd lean towards logistic regression.
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Karon
3 months ago
I'd go with random decision forests. They can handle high-dimensional data really well and are super flexible.
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Catarina
3 months ago
Logistic regression is the way to go here! It's perfect for binary classification tasks like this one.
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Tora
1 month ago
True, SVMs are another strong option for predicting binary outcomes.
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Filiberto
2 months ago
Support vector machines could also work well in this scenario.
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Jade
2 months ago
I agree, it's great for binary classification tasks with high-dimensional data.
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Freeman
2 months ago
Support vector machines could also work well for this scenario.
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Becky
2 months ago
Logistic regression is definitely a good choice for this kind of problem.
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Micaela
2 months ago
I agree, logistic regression is great for binary classification tasks.
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Edelmira
3 months ago
Logistic regression is definitely a good choice for this type of problem.
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