# Support Vector Machine

### What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?

SVM is a classification algorithm that seeks to determine a decision boundary by maximizing the distance between points of different classes.

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### How does SVM adjust for classes that cannot be linearly separated?

While the maximum margin classifier is optimal in theory, in practice, observations cannot be perfectly separated in most classification problems.

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### How does SVM work if no linear separation exists between classes?

While soft margin classification relaxes the requirement of a hyperplane that must perfectly distinguish between the classes, a separate issue arises when there is no way to define such a hyperplane in the original feature space.

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### What is the kernel trick in SVM?

The kernel trick allows SVM to form a decision boundary in higher dimensional space

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### What are common choices to use for kernels in SVM?

Common choices for kernels include: Linear, Polynomial, Radial Basis, Sigmoid

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### What are some of the pros/cons of SVM?

Pros: Relatively fast computational time due to kernel trick
Cons:Performance of algorithm is sensitive to the choice of kernel

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### Explain how SVM can be used in regression problems

While SVM is used most often in classification scenarios, it can be extended to regression cases by allowing the user to provide a maximum margin of error allowed for any observation.

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### Describe the hinge loss function used in SVM

The hinge loss function uses the distance between observations and the hyperplane that separates classes in the SVM algorithm in order to quantify the magnitude of error.

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### How does hinge loss differ from logistic loss?

Hinge loss adds an increased penalty to misclassifications that are off by a large amount, since the cost function increases linearly as the decision function output moves further away from the actual label.

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### What hyper-parameters are typically tuned in SVM?

C (regularization parameter), Kernel Function, Gamma (RBF kernel)

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