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

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Question: Which kernel is commonly used in SVM for non-linear data?

Options:

Correct Answer: Radial Basis Function (RBF) kernel

Solution:

The Radial Basis Function (RBF) kernel is commonly used in SVM to handle non-linear data effectively.

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

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

Step 1: Understand what SVM (Support Vector Machine) is. It is a type of machine learning algorithm used for classification and regression tasks.
Step 2: Know that SVM can work with both linear and non-linear data.
Step 3: Realize that non-linear data cannot be separated by a straight line.
Step 4: Learn that to handle non-linear data, SVM uses something called a 'kernel'.
Step 5: Identify the Radial Basis Function (RBF) kernel as a popular choice for non-linear data.
Step 6: Understand that the RBF kernel helps to transform the data into a higher dimension where it can be separated more easily.

No concepts available.