How does SVM behave with overlapping feature distributions?

SVM uses soft margins but cannot fully resolve heavy overlap without error.

When class distributions overlap significantly, SVM must trade off between margin size and misclassification. Slack variables allow controlled errors, but performance depends heavily on feature representation quality.