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Which kernel function is commonly used in SVM for non-linear classification?

Practice Questions

Q1
Which kernel function is commonly used in SVM for non-linear classification?
  1. Linear kernel
  2. Polynomial kernel
  3. Radial basis function (RBF) kernel
  4. Sigmoid kernel

Questions & Step-by-Step Solutions

Which kernel function is commonly used in SVM for non-linear classification?
  • Step 1: Understand what SVM (Support Vector Machine) is. It is a type of machine learning algorithm used for classification tasks.
  • Step 2: Know that SVM can work with both linear and non-linear data.
  • Step 3: Realize that for non-linear classification, we need a special function called a 'kernel' to help SVM separate the data.
  • Step 4: Learn that the Radial Basis Function (RBF) kernel is a commonly used kernel for non-linear classification.
  • Step 5: Remember that the RBF kernel helps to transform the data into a higher dimension where it can be separated more easily.
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