Answer / Shashi Shekhar Sisodiya

The kernel trick is a technique used in Support Vector Machines (SVM) to handle non-linearly separable data. It maps the input data from the original space into a higher dimensional feature space where the data becomes linearly separable. This transformation is done using a positive definite kernel function like polynomial, Gaussian radial basis function, or sigmoid. The kernel trick allows us to solve complex non-linear problems using SVM without having to explicitly find the mapped feature space.

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