Databricks Certified Professional Data Scientist Exam - Topic 5 Question 60 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam

Question #: 60
Topic #: 5

[All Databricks Certified Professional Data Scientist Questions]

Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.

Above is an example of

ALinear Regression

BLogistic Regression

CRecommendation system

DMaximum likelihood estimation

EHierarchical linear models

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Suggested Answer: A

by Cora at May 23, 2024, 04:34 PM

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Shasta

3 months ago

Really? I’m surprised it’s not a recommendation system!

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Frank

3 months ago

I thought it could be Linear Regression too, but I guess not.

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Quentin

3 months ago

Wait, isn’t it also about Maximum Likelihood Estimation?

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Corazon

4 months ago

Totally agree, binary outcomes scream logistic.

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Brigette

4 months ago

This is definitely about Logistic Regression!

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Dominic

4 months ago

I recall that logistic regression is specifically for binary outcomes, so I think that’s the right choice here.

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Michell

4 months ago

I’m a bit confused; I thought linear regression could also be used for binary outcomes, but now I’m leaning towards logistic regression.

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Tiffiny

4 months ago

I remember practicing a similar question where we had to determine the right model for a binary outcome. I think logistic regression fits here.

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Dusti

5 months ago

I think this might be logistic regression since the outcome is binary, but I'm not entirely sure.

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Kenneth

5 months ago

This is a straightforward logistic regression problem. The binary outcome and mix of predictors are a clear indication. I'll focus on setting up the logistic regression model, interpreting the coefficients, and evaluating the model fit.

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Daniel

5 months ago

Okay, I've got this. Logistic regression is the way to go. We're predicting a binary outcome (win/lose) based on continuous and categorical predictors. This is textbook logistic regression material.

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Irene

5 months ago

Hmm, I'm a bit unsure here. The wording about "factors that influence" makes me think this could also be a linear regression problem. But the binary outcome variable points more towards logistic regression. I'll have to think this through carefully.

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Buck

5 months ago

This looks like a classic logistic regression problem to me. The binary outcome variable and the mix of continuous and categorical predictors are a clear fit.

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Ozell

5 months ago

I'm a bit confused. The question mentions "maximum likelihood estimation" which makes me wonder if that's the right approach here. But the binary outcome variable still seems to point towards logistic regression. I'll have to review my notes on the different regression techniques to decide.

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Ronny

5 months ago

Hmm, I'm not totally sure about the details of Six Sigma. I'll have to think this through carefully.

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Bethanie

5 months ago

I'm a little confused by the wording of the question. I'll need to read it over a few times to make sure I understand.

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Raylene

5 months ago

I'm pretty sure the answer is C. The WADL allows you to outline XSD files, which seems like the most relevant option here.

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Fredric

10 months ago

Hierarchical linear models? Not even close! This is a classic binary outcome scenario, so logistic regression is the clear choice here.

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Tyra

9 months ago

It's all about predicting win or lose in this case.

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Lemuel

9 months ago

Yes, logistic regression is the way to go for binary outcomes.

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Krissy

9 months ago

Logistic Regression

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Callie

10 months ago

Maximum likelihood estimation? That's a bit of a stretch. While it's a useful technique in general, it's not the appropriate model for this particular problem. Logistic regression is the way to go.

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Esteban

8 months ago

I agree, maximum likelihood estimation is not the right approach for this scenario.

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Yoko

8 months ago

Definitely, logistic regression is the most suitable for binary outcomes.

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Vernell

8 months ago

Logistic regression is the best choice here.

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Jaleesa

8 months ago

E) Hierarchical linear models

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Valentin

9 months ago

D) Maximum likelihood estimation

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Nana

9 months ago

C) Recommendation system

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Brendan

9 months ago

B) Logistic Regression

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Taryn

10 months ago

A) Linear Regression

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Joana

10 months ago

Hmm, a recommendation system? I don't see how that's relevant to this problem. We're trying to model an election outcome, not make personalized recommendations.

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Cherri

11 months ago

Linear regression? Really? With a binary outcome? Doesn't sound quite right to me. Logistic regression is definitely the way to go here.

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Junita

9 months ago

It's all about predicting the likelihood of winning based on the predictor variables.

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Leatha

9 months ago

Logistic regression allows us to model the probability of a candidate winning.

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Heike

10 months ago

Linear regression wouldn't work well with a binary response variable.

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Brittney

10 months ago

I agree, logistic regression is the appropriate choice for a binary outcome.

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Nadine

11 months ago

I think this is a classic case of logistic regression. The outcome variable is binary, and we're trying to model the probability of a candidate winning based on various predictors. This makes perfect sense!

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