CertNexus Exam AIP-210 Topic 6 Question 21 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 21
Topic #: 6

You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?

AWhen it is necessary to save computational time.

BWhen the categories of the dependent variable are not linearly separable.

CWhen the distribution of the dependent variable is Gaussian.

DWhen there is high correlation among the features.

Show Suggested Answer

Suggested Answer: B

A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.

by Coleen at Jun 23, 2024, 06:11 AM

Limited Time Offer

25%

Off

Get Premium AIP-210 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Delpha

5 months ago

I believe using a polynomial kernel in that situation could help reduce overfitting.

upvoted 0 times

...

Clay

5 months ago

But wouldn't it also be useful when there is high correlation among the features?

upvoted 0 times

...

6 months ago

I'd go with B as well. A polynomial kernel can add flexibility to the SVM model, allowing it to handle more complex, non-linear patterns in the data. Definitely a better option than trying to shoehorn a linear model into a non-linear problem.

upvoted 0 times

Sharen

5 months ago

B) When the categories of the dependent variable are not linearly separable.

upvoted 0 times

...

Franchesca

5 months ago

A) When the categories of the dependent variable are not linearly separable.

upvoted 0 times

...

Leota

6 months ago

Option B seems like the obvious choice here. Using a polynomial kernel can introduce higher-order terms, which can help the SVM model better fit the non-linear decision boundary. Saves me from drawing a bunch of circles on the whiteboard to explain this to my colleagues.

upvoted 0 times

...

5 months ago

I agree, option B is the best choice for non-linear separable classes.

upvoted 0 times

...