Recall & Review
beginner
What is the main goal of a Support Vector Machine (SVM)?
The main goal of an SVM is to find the best boundary (called a hyperplane) that separates different groups (classes) in the data with the largest possible margin.
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beginner
What is a 'margin' in the context of SVM?
The margin is the distance between the hyperplane and the closest data points from each class. SVM tries to maximize this margin to improve the model's ability to generalize.
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beginner
What are 'support vectors' in SVM?
Support vectors are the data points closest to the hyperplane. They are critical because they define the position and orientation of the hyperplane.
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intermediate
How does SVM handle data that is not linearly separable?
SVM uses a technique called the 'kernel trick' to transform data into a higher dimension where it can be separated by a hyperplane, even if it is not separable in the original space.
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intermediate
What is the role of the 'C' parameter in SVM?
The 'C' parameter controls the trade-off between having a wide margin and correctly classifying training points. A small C allows more misclassifications but a wider margin, while a large C tries to classify all points correctly but may have a smaller margin.
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What does SVM try to maximize when finding the best hyperplane?
Which points influence the position of the SVM hyperplane?
What technique does SVM use to handle non-linear data?
What happens if the 'C' parameter in SVM is set very high?
Which of the following is NOT true about support vectors?
Explain in your own words how SVM finds the best boundary to separate two groups.
Describe how the kernel trick helps SVM deal with data that is not linearly separable.