BlackFriday 2024! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

CertNexus Exam AIP-210 Topic 6 Question 21 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 21
Topic #: 6
[All AIP-210 Questions]

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?

Show Suggested Answer Hide 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
4 months ago
I believe using a polynomial kernel in that situation could help reduce overfitting.
upvoted 0 times
...
Clay
4 months ago
But wouldn't it also be useful when there is high correlation among the features?
upvoted 0 times
...
Tracey
4 months ago
B is the way to go. Polynomial kernels are great for when your data looks more like a plate of spaghetti than a neat line. Gotta love those non-linear decision boundaries!
upvoted 0 times
Fabiola
3 months ago
B) B is the way to go.
upvoted 0 times
...
Francine
3 months ago
B) Gotta love those non-linear decision boundaries!
upvoted 0 times
...
Celeste
3 months ago
B) Polynomial kernels are great for when your data looks more like a plate of spaghetti than a neat line.
upvoted 0 times
...
Bonita
3 months ago
B) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
...
Rickie
4 months ago
I agree with Willodean, it can help capture non-linear relationships in the data.
upvoted 0 times
...
Willodean
4 months ago
I think using a polynomial kernel could help when the categories are not linearly separable.
upvoted 0 times
...
Salome
5 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
4 months ago
B) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
Franchesca
4 months ago
A) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
...
Leota
5 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
...
Mariann
5 months ago
I think option B is the correct answer. A polynomial kernel can help capture non-linear relationships between the features and the target variable, allowing for better classification when the classes are not linearly separable.
upvoted 0 times
Dominque
4 months ago
Yes, option B is the way to go for non-linear separable categories.
upvoted 0 times
...
Yolande
4 months ago
I think option B makes sense, it helps capture those non-linear relationships.
upvoted 0 times
...
Oren
5 months ago
I agree, option B is the best choice for non-linear separable classes.
upvoted 0 times
...
...

Save Cancel