Svm Classification

Question

What can be the theoretical reason/s for which a support vector machine with linear kernel (implemented with LinearSVC from sklearn) fails miserably in a multi-class (5 classes) classification?

Perhaps more suitable in Stat or Crossvalidation. – I was suspended for talking May 27 '18 at 19:16 — I was suspended for talking, May 27 '18 at 19:16

score 1 · Answer 1 · answered May 27 '18 at 19:30

1

An SVM assumes that a separating hyperplane classifies your data well. If no separating hyperplane exists, then an SVM will not perform well.

Or, there's a bug in your code.

answered May 27 '18 at 19:30

NicNic8

6,951

score 1 · Answer 2 · answered May 27 '18 at 19:59

1

A Support Vector Machine only separates binarily. Discriminates between two classes. Or between belonging or not belonging to one class. For more than two classes you will need to be inventive to get it to make sense.

answered May 27 '18 at 19:59

mathreadler

25,824

Svm Classification

2 Answers2