SVM - hard or soft margins?

Question

Given a linearly separable dataset, is it necessarily better to use a hard margin SVM over a soft-margin SVM?

Answer 1

I’d say that Soft Margin SVM is better than Hard Margin SVM, because:

• Hard Margin SVM is quite sensitive to outliers.

• Soft Margin SVM  avoids iterating over outliers.

In the below diagram you can notice overfitting of hard margin SVM.

Soft-margin SVM can choose a decision boundary that has non-zero training error even if the dataset is linearly separable, and is less likely to overfit. You can notice that decreasing C value causes the classifier to leave linear separability in order to gain stability. 

You need to set the best value of C in SVM. The more optimal value of C will give you more accuracy.

Hope this answer helps.