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MMLU / Which of the following is/are true regarding an SVM?

Problem

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question

Which of the following is/are true regarding an SVM?

choices

  1. For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line.
  2. In theory, a Gaussian kernel SVM cannot model any complex separating hyperplane.
  3. For every kernel function used in a SVM, one can obtain an equivalent closed form basis expansion.
  4. Overfitting in an SVM is not a function of number of support vectors.

subject

machine_learning

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Source and history

task_id
fadd2364-94dd-5965-859f-0f251c49a6ae
task_key
all--test--fadd2364-94dd-5965-859f-0f251c49a6ae
task_revision_id
1
config / split
all / test

Official source

initial import