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:

AApriori Algorithm

BRidge and Lasso

COrdinary Least squares

DInteger programming

Show Suggested Answer

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, 06:44 AM

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Donette

3 months 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

2 months ago

C) Ordinary Least squares

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Lynette

2 months ago

B) Ridge and Lasso

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Amie

2 months ago

A) Apriori Algorithm

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Phung

3 months ago

C) Ordinary Least Squares? More like Ordinary Genius Squares, am I right? Nailed it!

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Dannette

3 months 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

3 months ago

D) Integer programming? What is this, a trick question? C) Ordinary Least Squares is definitely the right choice.

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Micaela

2 months ago

B) Ridge and Lasso are not the methods used for solving coefficients in linear regression.

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Elli

2 months ago

A) Apriori Algorithm is not used for solving coefficients in linear regression.

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Elli

2 months ago

D) Integer programming is not the right choice for this.

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Lindy

2 months ago

C) Ordinary Least squares is the method used to solve for coefficients in linear regression.

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Colette

3 months ago

Ooh, A) Apriori Algorithm? That's for association rules, not linear regression. C) is the correct answer here.

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Marya

3 months 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

2 months ago

B) Ridge and Lasso

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Linwood

3 months ago

A) Apriori Algorithm

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Blythe

3 months ago

C) Ordinary Least squares

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Peggie

4 months ago

I remember learning about Ordinary Least squares in my data science course, so I'll stick with C)

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Lourdes

4 months ago

I would go with B) Ridge and Lasso, it helps with regularization

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Stefania

4 months ago

I think it's C too, it's a common method for linear regression

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Tashia

4 months 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

2 months ago

C) Ordinary Least squares

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Glenna

2 months ago

B) Ridge and Lasso

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Kip

3 months ago

A) Apriori Algorithm

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Nada

3 months ago

C) Ordinary Least squares

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Amber

3 months ago

B) Ridge and Lasso

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Ailene

3 months ago

A) Apriori Algorithm

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Tyisha

4 months ago

C) Ordinary Least squares

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