New Year Sale ! Hurry Up, Grab the Special Discount - Save 25% - Ends In 0d 10h 9m 6s Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

CertNexus Exam AIP-210 Topic 3 Question 23 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 23
Topic #: 3
[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.


Contribute your Thoughts:

Meghan
4 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
4 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
4 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
3 months ago
B) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
Christa
3 months ago
A) When it is necessary to save computational time.
upvoted 0 times
...
...
Zena
5 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
5 months ago
But wouldn't it also be useful when there is high correlation among the features?
upvoted 0 times
...
Elenor
5 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
3 months ago
C) When the distribution of the dependent variable is Gaussian.
upvoted 0 times
...
Lavera
3 months ago
B) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
Chan
3 months ago
C) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
Laticia
3 months ago
A) When it is necessary to save computational time.
upvoted 0 times
...
Lauran
4 months ago
B) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
Twanna
4 months ago
A) When the categories of the dependent variable are not linearly separable.
upvoted 0 times
...
...
Reita
5 months ago
I agree with Brigette, it can help capture non-linear relationships in the data.
upvoted 0 times
...
Vallie
5 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
4 months ago
Yes, it's a good choice when the categories are not linearly separable.
upvoted 0 times
...
Alaine
4 months ago
I agree, using a polynomial kernel can definitely help capture those non-linear relationships.
upvoted 0 times
...
...
Brigette
5 months ago
I think using a polynomial kernel would help when the categories are not linearly separable.
upvoted 0 times
...

Save Cancel
az-700  pass4success  az-104  200-301  200-201  cissp  350-401  350-201  350-501  350-601  350-801  350-901  az-720  az-305  pl-300  

Warning: Cannot modify header information - headers already sent by (output started at /pass.php:70) in /pass.php on line 77
a