When are soft margin classifiers useful?

In this lesson, we looked at an example in which a soft margin linear SVM (low cost, wide margin) had a better accuracy than its hard margin counterpart (high cost, narrow margin). Which of the phrases listed best completes the following statement:

Linear soft margin classifiers are most likely to be useful when:

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Possible Answers

Working with a linearly separable dataset.
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Dealing with a dataset that has a highly nonlinear decision boundary.
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Working with a dataset that is almost linearly separable.
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