Practical Data Science using Python - Support Vector Machine Concepts

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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is one of the key strengths of Support Vector Machines (SVM)?
They are not suitable for high-dimensional data.
They perform well with complex datasets and few observations.
They can only handle linear datasets.
They require a large number of observations.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does SVM aim to find between different classes in a dataset?
The narrowest path
The most complex path
The widest possible path
The shortest path
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In SVM, what is the decision boundary also known as?
Gradient descent line
Feature scale
Optimal separating hyperplane
Support vector
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main goal of large margin classification in SVM?
To maximize the distance between classes
To increase the number of support vectors
To minimize the number of features
To reduce the complexity of the model
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is scaling important when using SVM?
It reduces the number of features.
It increases the number of support vectors.
It simplifies the gradient descent process.
It makes the model more complex.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the effect of adding more training instances on the same side of the margin in SVM?
It does not affect the model.
It increases the number of support vectors.
It changes the decision boundary.
It affects the variance of the model.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are support vectors in the context of SVM?
Points that are far from the margin
Points that are randomly selected
Points that determine the width of the margin
Points that are ignored during training
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