Which kernel function is commonly used in SVM to handle non-linear data?

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Question: Which kernel function is commonly used in SVM to handle 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 by mapping it into a higher-dimensional space.

Which kernel function is commonly used in SVM to handle non-linear data?

Which kernel function is commonly used in SVM to handle non-linear data?

Step 1: Understand that SVM (Support Vector Machine) is a type of machine learning algorithm used for classification tasks.
Step 2: Know that sometimes data is not linearly separable, meaning you can't draw a straight line to separate different classes.
Step 3: To handle non-linear data, we need a way to transform it into a space where it can be separated by a straight line.
Step 4: The Radial Basis Function (RBF) kernel is a mathematical function that helps in this transformation.
Step 5: The RBF kernel takes the original data and maps it into a higher-dimensional space, making it easier to separate the classes.

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