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‘svm’ Interview Questions

What are some of the pros/cons of SVM?

Explain how SVM can be used in regression problems

How does hinge loss differ from logistic loss?

Describe the hinge loss function used in SVM

What hyper-parameters are typically tuned in SVM?

What are common choices to use for kernels in SVM?

What is the kernel trick in SVM?

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

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

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

Among the common classification algorithms, which all produce probability estimates in addition to class labels?

What are some of the common algorithms used for classification?

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