Support Vector Machine, Machine Learning, Artificial Intelligence, Data Science, Ambarish 224.
feature space by the kernel trick. In summary, nonlinear SVM classification is useful when the data cannot be separated by a linear boundary. The kernel trick allows us to transform the input data into a higher-dimensional space where it becomes linearly separable, which can be achieved by kernel