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Databricks Exam Databricks Certified Professional Data Scientist Topic 2 Question 65 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 65
Topic #: 2
[All Databricks Certified Professional Data Scientist Questions]

Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

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Suggested Answer: C

In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age and education. In other words, the fitted model should minimize the overall error between the linear model and the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters


by Hoa at Jul 14, 2024, 07:44 AM

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Derick
2 days ago
I thought it was Ridge and Lasso for sure.
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Tomas
8 days ago
It's definitely Ordinary Least Squares!
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Felix
14 days ago
I feel like I’ve seen similar questions before, and I’m leaning towards Ordinary Least Squares as the correct method.
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Sonia
19 days ago
The Apriori Algorithm seems more related to association rules, so I doubt that's the right answer for this question.
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Weldon
24 days ago
I remember practicing with Ridge and Lasso, but I don't think they directly solve for the coefficients like OLS does.
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Suzan
1 month ago
I think the Ordinary Least Squares method is what we used to find those coefficients, but I'm not entirely sure.
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Alita
1 month ago
Apriori algorithm? Integer programming? Those don't seem right for a linear regression problem. I'm going to eliminate those options and focus on the regression-specific choices, C and B.
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Elbert
1 month ago
Okay, I remember learning about different regression techniques in class. I think Ridge and Lasso are used for regularization, so the answer is probably C, Ordinary Least Squares.
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Ines
1 month ago
Hmm, I'm a bit unsure about this one. I know linear regression is used to find the coefficients, but I can't remember the specific method. I'll have to think this through carefully.
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Benedict
1 month ago
This looks like a straightforward linear regression question. I'm pretty confident the answer is C, Ordinary Least Squares.
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Vallie
1 month ago
I think the Blue Ocean editor not showing the 'post' section is correct, but I’m a bit confused about how to modify it in the code editor.
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Irma
1 month ago
I think we should definitely consider updating the image when a new application release comes out, right? That's what we discussed in our last study group.
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Ashleigh
1 month ago
Hmm, I'm a bit unsure about this one. The options seem to be describing different quality management techniques, but I'm not sure which one is specifically designed for organization-wide understanding and responsiveness.
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Donette
1 year ago
B) Ridge and Lasso? More like Ridge and Flossy, am I right? Just kidding, C) is the way to go.
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Terry
1 year ago
C) Ordinary Least squares
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Lynette
1 year ago
B) Ridge and Lasso
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Amie
1 year ago
A) Apriori Algorithm
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Phung
1 year ago
C) Ordinary Least Squares? More like Ordinary Genius Squares, am I right? Nailed it!
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Dannette
1 year ago
I'm going to have to go with C) Ordinary Least Squares. It's the go-to method for linear regression, no doubt about it.
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Charisse
1 year ago
D) Integer programming? What is this, a trick question? C) Ordinary Least Squares is definitely the right choice.
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Micaela
1 year ago
B) Ridge and Lasso are not the methods used for solving coefficients in linear regression.
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Elli
1 year ago
A) Apriori Algorithm is not used for solving coefficients in linear regression.
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Elli
1 year ago
D) Integer programming is not the right choice for this.
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Lindy
1 year ago
C) Ordinary Least squares is the method used to solve for coefficients in linear regression.
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Colette
1 year ago
Ooh, A) Apriori Algorithm? That's for association rules, not linear regression. C) is the correct answer here.
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Marya
1 year ago
B) Ridge and Lasso are great options for regularization, but for the basic linear regression coefficients, C) is the way to go.
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Temeka
1 year ago
B) Ridge and Lasso
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Linwood
1 year ago
A) Apriori Algorithm
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Blythe
1 year ago
C) Ordinary Least squares
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Peggie
1 year ago
I remember learning about Ordinary Least squares in my data science course, so I'll stick with C)
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Lourdes
1 year ago
I would go with B) Ridge and Lasso, it helps with regularization
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Stefania
1 year ago
I think it's C too, it's a common method for linear regression
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Tashia
1 year ago
C) Ordinary Least Squares is the way to go. It's the classic method for solving linear regression models.
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Virgie
1 year ago
C) Ordinary Least squares
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Glenna
1 year ago
B) Ridge and Lasso
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Kip
1 year ago
A) Apriori Algorithm
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Nada
1 year ago
C) Ordinary Least squares
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Amber
1 year ago
B) Ridge and Lasso
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Ailene
1 year ago
A) Apriori Algorithm
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Tyisha
1 year ago
C) Ordinary Least squares
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