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What is the Kernel SVM ?
Kernel is the function used for the help in Support vector machine algorithms helping to solve problems. It makes an easier calculation instead of complex calculation. The amazing thing about kernel is that it allows us to go to higher dimensions and execute smooth calculations.
In SVM, the kernel functions are extremely significant. Their role is to accept data as input and transform it into whichever format is required. They are vital in SVM since they assist in determining a variety of important factors.
How does it is Work ?
Kernels are a type of linear classifier that can be used to handle non-linear issues. This is also called the kernel trick method. In SVM codes, the kernel functions are employed as parameters. They aid in the formation of the hyperplane and decision boundary. we can set the kernel value in SVM.
Any form of kernel, from linear to polynomial, can be used as the value. The decision boundary will be linear and two-dimensional if the kernel value is linear. These kernels function in the establishment of decision boundaries in higher dimensions.
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