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Databricks Exam Databricks Certified Professional Data Scientist Topic 1 Question 42 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 42
Topic #: 1
[All Databricks Certified Professional Data Scientist Questions]

Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

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Suggested Answer: C

by Justine at May 29, 2023, 08:08 PM

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Diane
4 months ago
Logistic regression is like a magician's hat - it can pull out both numerical and categorical rabbits!
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Dallas
2 months ago
D) None of the 1 and 2 are correct
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Mozell
3 months ago
C) Both 1 and 2 are correct
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Janet
3 months ago
B) Categorical
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Coleen
3 months ago
A) Numerical
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Avery
4 months ago
Ah, a trick question! The answer is obvious - logistic regression can work with both numerical and categorical variables. Easy peasy!
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Lashandra
2 months ago
C) Both 1 and 2 are correct
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Eva
2 months ago
B) Categorical
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Lonny
3 months ago
A) Numerical
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Barb
4 months ago
Hmm, let me think... Logistic regression definitely supports both types of predictor variables. This is a well-known fact in the field of machine learning.
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Jamal
3 months ago
Absolutely, having the flexibility to work with both types of variables makes logistic regression a versatile tool in data analysis.
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Otis
3 months ago
That's correct. It's one of the reasons why logistic regression is a popular choice for predictive modeling.
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Vincent
3 months ago
Absolutely, having the flexibility to work with both types of variables makes logistic regression a powerful tool for prediction.
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Providencia
3 months ago
Yes, you're right. Logistic regression can handle both numerical and categorical variables.
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Josphine
3 months ago
That's correct! It's one of the reasons why logistic regression is widely used in predictive modeling.
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Karon
4 months ago
Yes, logistic regression can handle both numerical and categorical predictor variables.
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Boris
5 months ago
Ah, this is a classic question. I'm pretty sure the correct answer is C - both numerical and categorical variables can be used in logistic regression.
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Chery
3 months ago
No problem, happy to help clarify things.
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Hollis
3 months ago
That's interesting, I didn't know that. Thanks for sharing!
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Velda
3 months ago
Yes, you're right. Logistic regression can use both numerical and categorical variables.
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Tien
3 months ago
I think the correct answer is C) Both 1 and 2 are correct.
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Nelida
4 months ago
It's important to understand the types of variables that can be used in different models.
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Helene
4 months ago
That's good to know. I always get confused with these types of questions.
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Marsha
4 months ago
Yes, you're right. Logistic regression can use both numerical and categorical variables.
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Loreta
4 months ago
I think the correct answer is C) Both 1 and 2 are correct.
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Mattie
5 months ago
Actually, I think the correct answer is C) Both 1 and 2 are correct because logistic regression can use both numerical and categorical variables.
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Amie
5 months ago
I disagree, I believe the answer is B) Categorical.
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Mattie
5 months ago
I think the answer is A) Numerical.
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Gilberto
5 months ago
Of course, logistic regression can handle both numerical and categorical variables! How else would we be able to model complex real-world scenarios?
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Franchesca
4 months ago
Of course, logistic regression can handle both numerical and categorical variables! How else would we be able to model complex real-world scenarios?
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Jolene
4 months ago
C) Both 1 and 2 are correct
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Chau
4 months ago
B) Categorical
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Novella
4 months ago
A) Numerical
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Wynell
5 months ago
Actually, I think the correct answer is C) Both 1 and 2 are correct because logistic regression can use both numerical and categorical variables.
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Crista
5 months ago
I disagree, I believe the answer is B) Categorical.
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Wynell
5 months ago
I think the answer is A) Numerical.
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