Databricks Certified Professional Data Scientist Exam - Topic 5 Question 76 Discussion

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

Question #: 76
Topic #: 5

[All Databricks Certified Professional Data Scientist Questions]

Which of the following statement is true for the R square value in the regression model?

AWhen R square =1 , all the residuals are equal to 0

BWhen R square =0, all the residual are equal to 1

CR square can be increased by adding more variables to the model.

DR-squared never decreases upon adding more independent variables.
R square can be made high, it means when we add more variables R-square will increase. And R-square will never decreases if you add more independent variables. Higher R square value can have lower the residuals.

Show Suggested Answer

Suggested Answer: C

by Luz at Dec 05, 2024, 06:03 PM

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Sunny

3 months ago

B is definitely false, residuals aren't always equal to 1.

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Vinnie

3 months ago

C is misleading, just adding variables doesn't guarantee a better model.

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Daniela

3 months ago

Wait, can R square really never decrease? Sounds fishy.

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Teddy

4 months ago

Totally agree, D is spot on too!

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Aliza

4 months ago

A is true, R square = 1 means perfect fit!

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Destiny

4 months ago

I’m pretty sure that when R square = 0, it doesn’t mean all residuals are equal to 1. That doesn't sound right at all!

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Kanisha

4 months ago

I feel like I’ve seen a question about R square never decreasing with added variables before, but I’m not confident about the details.

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Annice

4 months ago

I think R square can be increased by adding more variables, but I also recall that it doesn't always mean a better model.

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Ma

5 months ago

I remember that R square = 1 means the model fits perfectly, so all residuals should be 0, right? But I'm not completely sure.

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Robt

5 months ago

Ah, I think I've got it. When R-squared is 1, that means the model perfectly fits the data, so all the residuals would be 0. And when R-squared is 0, that means the model doesn't explain any of the variance, so the residuals would all be 1. I'll mark that down as my answer.

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Orville

5 months ago

I'm a bit confused on this one. I know R-squared is a measure of goodness of fit, but I'm not sure about the specific relationship between R-squared and the residuals. I'll need to review my notes on regression analysis.

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Jody

5 months ago

Okay, let's see. I know that R-squared represents the proportion of the variance in the dependent variable that is explained by the independent variables in the model. So I'll need to consider how that relates to the residuals.

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Stevie

5 months ago

Hmm, this is a tricky one. I'll need to think carefully about the properties of R-squared and how they relate to the residuals.

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Soledad

9 months ago

Hey, I've got a great idea – let's add a million variables to the model and get that R-squared up to 99.99%! Residuals? Who needs 'em?

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Dyan

8 months ago

It's important to strike a balance between adding variables and maintaining model accuracy.

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Alayna

8 months ago

True, adding more variables can lead to overfitting and decrease the model's accuracy.

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Dyan

8 months ago

Yeah, R square can be inflated by adding unnecessary variables.

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Leila

8 months ago

That's not how it works. Adding more variables doesn't always increase R square.

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Kenny

10 months ago

Higher R-squared means lower residuals? Well, duh. Isn't that the whole point of regression modeling? This question is making me feel like I'm back in kindergarten.

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Marshall

9 months ago

It's all about minimizing those residuals to get a good fit.

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Cecily

9 months ago

Adding more variables can definitely help increase the R-squared value.

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Alesia

9 months ago

Exactly! The higher the R-squared, the better the model fits the data.

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Rodrigo

10 months ago

Ah, I see what they're getting at with the 'R-squared never decreases' bit. But adding more variables just to boost R-squared? That's a rookie move, my dude.

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