We know that linear kernel SVM may give better results than logistic regression since maximizing the margin usually leads to more stable results/better displacement of the decision boundary. But is there any scenario in which a linear kernel SVM performs worse than a logistic regression with respect to test accuracy?

  • $\begingroup$ Define "better"/"worse". By what metric? $\endgroup$
    – D.W.
    Jan 8, 2023 at 2:12
  • $\begingroup$ As written, with respect to test accuracy, meaning that lower test accuracy results in worse results $\endgroup$
    – DaSim
    Jan 9, 2023 at 9:27

1 Answer 1


SVM may perform worse than Logistic Regression when the dataset is small, thus data points near the decision boundary (Support Vectors) may not be a true representation of the actual decision boundary, and thus may form a false maximum margin classifier boundary.
I don't have any dataset example in mind but theoretically, that should be it


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.