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Databricks Exam Databricks Certified Professional Data Scientist Topic 5 Question 80 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 80
Topic #: 5
[All Databricks Certified Professional Data Scientist Questions]

What describes a true property of Logistic Regression method?

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Suggested Answer: D

by Lucille at Mar 07, 2025, 01:14 PM

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Mel
2 days ago
Logistic regression doesn't handle missing values well.
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Malcom
8 days ago
I recall that logistic regression is used for predicting probabilities, but I'm not sure if it specifically handles discontinuous effects well, so D is tricky.
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Ahmed
13 days ago
I’m not entirely sure, but I think logistic regression can struggle with correlated variables, which makes C questionable.
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Erick
19 days ago
I feel like logistic regression is more suited for binary outcomes rather than discrete variables with many values, so B seems off.
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Johana
24 days ago
I remember that logistic regression is not great with missing values, so I think A might be wrong.
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Edgar
30 days ago
I think option D might be the best answer, as Logistic Regression assumes a continuous, linear relationship between the predictors and the outcome. Discontinuous effects could be problematic.
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Samira
1 month ago
Logistic Regression is known to be sensitive to correlated variables, so option C doesn't sound right to me. I'll have to double-check that.
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Elvera
1 month ago
I remember from class that Logistic Regression works best with discrete variables that have a limited number of distinct values, so option B is likely not correct.
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Tawny
1 month ago
Hmm, I'm not sure about the other options. I'll need to think through the properties of Logistic Regression more carefully.
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Precious
1 month ago
I'm pretty confident that Logistic Regression doesn't handle missing values well, so I'll rule out option A.
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Lenita
1 month ago
Ugh, I'm a bit lost here. I know logistic regression is used for classification, but I'm not sure about the specifics of how it handles different types of variables and data issues. I'll have to make an educated guess on this one.
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Oren
1 month ago
I'm pretty confident on this one. Logistic regression doesn't handle missing values very well, and it works best with discrete variables that don't have too many distinct values. I'll go with option B.
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Earleen
1 month ago
Okay, let's see. I know logistic regression is good for binary outcomes, but I'm not sure about how it handles missing values or different types of variables. I'll have to review my notes on the key assumptions and characteristics.
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Leonie
1 month ago
Hmm, this is a tricky one. I'll need to think carefully about the properties of logistic regression to determine which one is true.
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Adelaide
6 months ago
Logistic regression? More like illogical regression, am I right? But hey, at least it's not as bad as trying to use linear regression for binary outcomes. That's just plane wrong!
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Ressie
5 months ago
D) It works well with variables that affect the outcome in a discontinuous way.
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Tonette
5 months ago
C) It is robust with redundant variables and correlated variables.
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Eliseo
5 months ago
B) It works well with discrete variables that have many distinct values.
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Reita
5 months ago
A) It handles missing values.
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Kallie
6 months ago
D? Really? That can't be correct. Logistic regression is all about modeling continuous, smooth relationships. Discontinuous effects are a no-go!
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Cecilia
6 months ago
This is a tricky one. I would have thought A, but now I'm second-guessing myself. Logistic regression must handle missing values well, right? Maybe I need to review my notes again.
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Sean
5 months ago
C) It is robust with redundant variables and correlated variables.
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Ariel
5 months ago
B) It works well with discrete variables that have many distinct values.
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Alpha
5 months ago
A) It handles missing values well.
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Lucina
6 months ago
Hmm, I'm leaning towards B. Logistic regression can handle discrete variables with lots of values better than other methods. Though I guess C could also be true.
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Catarina
5 months ago
User 3: I'm not sure, but I think B and C both make sense in different scenarios.
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Amie
5 months ago
User 2: I agree, but C could also be true. Logistic regression is robust with redundant and correlated variables.
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Merrilee
5 months ago
User 1: I think B is the correct answer. Logistic regression works well with discrete variables with many distinct values.
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Leonora
7 months ago
I'm pretty sure the correct answer is C. Logistic regression is known to be robust with redundant and correlated variables. The other options just don't sound right.
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Ty
5 months ago
Hmm, I see your point. But I still think it's D. It works well with variables that affect the outcome in a discontinuous way.
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Samira
6 months ago
Actually, I'm pretty sure it's C. Logistic regression is robust with redundant and correlated variables.
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Ricarda
6 months ago
No, I believe it's B. It works well with discrete variables that have many distinct values.
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Irma
6 months ago
I think it's A. Logistic Regression handles missing values well.
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Judy
7 months ago
Hmm, that makes sense too. Logistic Regression can handle correlated variables effectively.
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Mirta
7 months ago
I disagree, I believe the answer is C) It is robust with redundant variables and correlated variables.
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Judy
7 months ago
I think the answer is B) It works well with discrete variables that have many distinct values.
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