CertNexus AIP-210 Exam - Topic 5 Question 39 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 39
Topic #: 5

Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?

AClustering

BLinear regression

CPolynomial regression

DRidge regression

Show Suggested Answer

Suggested Answer: C, E

Lasso regression and ridge regression are both types of linear regression models that can handle high-dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. Reference: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]

by Kaycee at Mar 08, 2025, 02:50 AM

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Thaddeus

6 months ago

I agree, ridge regression is the best option here!

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Yan

7 months ago

Wait, can polynomial regression really help with that?

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Gladis

7 months ago

Definitely not clustering, that’s for sure!

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Chun

7 months ago

I think linear regression can handle it too, right?

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Merilyn

7 months ago

Ridge regression is the way to go for collinearity!

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Raul

7 months ago

I have a vague memory of clustering being related to grouping data, but I don't think it addresses collinearity directly like ridge regression does.

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Nettie

8 months ago

I practiced a question similar to this, and I think ridge regression is specifically designed to handle multicollinearity issues.

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Harrison

8 months ago

I'm not entirely sure, but I feel like linear regression might struggle with collinearity. Maybe it's ridge regression that helps?

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Celestine

8 months ago

I remember we discussed collinearity in class, and I think ridge regression was mentioned as a solution for that.

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Darrel

8 months ago

I'm a bit confused by this question. Clustering doesn't seem relevant to the issue of collinearity. Linear and polynomial regression don't directly address that problem either. I think D, ridge regression, is the best option here, but I'm not 100% sure.

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Jaclyn

8 months ago

Okay, let me see. Polynomial regression can handle nonlinear relationships, but that's not the same as collinearity. Linear regression on its own doesn't really address collinearity either. I'm leaning towards D, ridge regression, since that's specifically designed to deal with multicollinearity.

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Louisa

8 months ago

Hmm, I'm a bit unsure about this one. I know clustering is used for grouping similar data points, but I'm not sure how that would help with collinearity. I'll have to think this through a bit more.

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Otis

8 months ago

I think this is asking about dealing with multicollinearity in regression models. If that's the case, I'd go with D. Ridge regression is a good option to handle that issue.

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Shawana

1 year ago

Clustering? What is this, a middle school science fair project? Ridge regression is the grown-up way to handle multicollinearity.

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Julian

11 months ago

Linear regression is a good starting point, but Ridge regression is more robust for multicollinearity.

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Leota

12 months ago

Clustering is more about grouping data points, not really for handling collinearity.

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Brittni

12 months ago

Polynomial regression can also help capture non-linear relationships among variables.

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Wilda

1 year ago

Ridge regression is definitely the way to go for handling collinearity.

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Alesia

1 year ago

Linear regression? Really? That's like trying to fit a square peg into a round hole. Ridge regression is the way to go when you've got collinearity issues.

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Micaela

1 year ago

Polynomial regression? Seriously? That's like trying to fix a leaky faucet with duct tape. Ridge regression is the obvious solution to this problem.

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Blair

12 months ago

Clustering and Linear regression may not be as effective as Ridge regression in this scenario.

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Karina

1 year ago

I agree, Ridge regression is designed to handle near-linear relationships among the independent variables.

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Loreta

1 year ago

Polynomial regression is not the best option here. Ridge regression is more suitable for dealing with collinearity.

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Dante

1 year ago

Ah, the age-old question of how to handle those pesky multicollinear variables. D) Ridge regression is the clear choice here. It's like a gentle hug for your model, keeping it from falling apart.

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Jamal

1 year ago

Ridge regression is like a safety net for your model.

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Bette

1 year ago

I agree, Ridge regression helps with collinearity.

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Elke

1 year ago

I think D) Ridge regression is the way to go.

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Erick

1 year ago

Ridge regression is the way to go when dealing with collinearity. It's like using crutches for your independent variables - they get the support they need to walk straight.

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