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‘Support Vector Machine’ Interview Questions

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

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

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

What is the kernel trick in SVM?

What are common choices to use for kernels in SVM?

What are some of the pros/cons of SVM?

Explain how SVM can be used in regression problems

Describe the hinge loss function used in SVM

How does hinge loss differ from logistic loss?

What hyper-parameters are typically tuned in SVM?

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