How does SVM behave in high noise datasets?

SVM performance degrades in high noise but can be stabilized using soft margins and tuning.

Noise increases overlap between classes, causing support vectors to include noisy points. Soft margin SVM reduces sensitivity using slack variables and lower C values. However, excessive noise may still require robust preprocessing or alternative models.