CertNexus Exam AIP-210 Topic 3 Question 23 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 23
Topic #: 3

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 Aliza at Jul 10, 2024, 04:19 PM

Limited Time Offer

25%

Off

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

Contribute your Thoughts:

Submit Cancel

Meghan

3 months ago

B is the way to go. Polynomial kernels can handle the complexity of non-linearly separable data. Easy peasy lemon squeezy!

upvoted 0 times

...

Vi

3 months ago

I'm going to have to go with B on this one. Polynomial kernels are designed to tackle non-linear problems, so that seems like the obvious choice here.

upvoted 0 times

...

Rasheeda

3 months ago

I'm going with B as well. Polynomial kernels are great for handling non-linear data, which is exactly the situation described in the question.

upvoted 0 times

Stephaine

2 months ago

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

upvoted 0 times

...

Christa

2 months ago

A) When it is necessary to save computational time.

upvoted 0 times

...

Zena

4 months ago

I believe using a polynomial kernel can help in that situation as well, by introducing non-linearity to the decision boundary.

upvoted 0 times

...

Ricki

4 months ago

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

upvoted 0 times

...

Elenor

4 months ago

Definitely B. The polynomial kernel can model more complex decision boundaries, which is crucial when the classes are not linearly separable.

upvoted 0 times

Tora

2 months ago

C) When the distribution of the dependent variable is Gaussian.

upvoted 0 times

...

Lavera

2 months ago

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

upvoted 0 times

...

Chan

2 months ago

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

upvoted 0 times

...

Laticia

2 months ago

A) When it is necessary to save computational time.

upvoted 0 times

...

Lauran

3 months ago

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

upvoted 0 times

...

Twanna

3 months ago

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

upvoted 0 times

...

Reita

4 months ago

I agree with Brigette, it can help capture non-linear relationships in the data.

upvoted 0 times

...

Vallie

4 months ago

I think B is the correct answer. The polynomial kernel can help capture non-linear relationships in the data, which is useful when the categories are not linearly separable.

upvoted 0 times

Margarett

3 months ago

Yes, it's a good choice when the categories are not linearly separable.

upvoted 0 times

...

Alaine

3 months ago

I agree, using a polynomial kernel can definitely help capture those non-linear relationships.

upvoted 0 times

...

Brigette

4 months ago

I think using a polynomial kernel would help when the categories are not linearly separable.

upvoted 0 times

...