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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?
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Which kernel function is commonly used in SVM to handle non-linear data?
Linear kernel
Polynomial kernel
Radial basis function (RBF) kernel
Sigmoid kernel
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The Radial Basis Function (RBF) kernel is commonly used in SVM to handle non-linear data by mapping it into a higher-dimensional space.
Questions & Step-by-step Solutions
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Q: Which kernel function is commonly used in SVM to handle non-linear data?
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.
Steps: 5
Show Steps
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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