The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

Machine Learning Resources

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

Bookmark this question

While the maximum margin classifier is optimal in theory, in practice, observations cannot be perfectly separated in most classification problems. Therefore, expecting to define a hyperplane that perfectly separates between classes with no misclassifications is not realistic. Instead of using a hard margin classifier, SVM uses a soft margin that allows for some misclassifications in the training process. The extent to which the algorithm is allowed to have misclassifications is controlled by a regularization parameter C and is typically tuned during cross validation. This issue is another example of the bias/variance tradeoff that occurs throughout machine learning, as the soft margin classifier introduces some bias with the hope of reducing variance when classifying future observations. In the case of a soft margin classifier, the support vector includes observations both on and within the margins. 

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics

Partner Ad

Learn Data Science with Travis - your AI-powered tutor |