Answer / Lalit Kumar

Kernel Support Vector Machines (SVMs) are a popular machine learning algorithm used for classification and regression problems. They work by finding the optimal hyperplane that separates data points of different classes in high-dimensional feature spaces. When the original data cannot be linearly separated, SVMs use a kernel function to map the data into a higher dimensional space where it can be separated more easily. Common kernels include linear, polynomial, radial basis function (RBF), and sigmoid.

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