we can observe from the above output that an svm classifier fit to the data with margins dashed lines and support vectorsthe pivotal elements of this fittouching the dashed line these support vector points are stored in the support_vectors_ attribute of the classifier as followsmodel support_vectors_ the output is as followsarray([[ ][ ][ ]]svm kernels in practicesvm algorithm is implemented with kernel that transforms an input data space into the required form svm uses technique called the kernel trick in which kernel takes low dimensional input space and transforms it into higher dimensional space in simple wordskernel converts non-separable problems into separable problems by adding more dimensions to it it makes svm more powerfulflexible and accurate the following are some of the types of kernels used by svmlinear kernel it can be used as dot product between any two observations the formula of linear kernel is as belowk(xxi sum( xi from the above formulawe can see that the product between two vectors say xi is the sum of the multiplication of each pair of input values polynomial kernel it is more generalized form of linear kernel and distinguish curved or nonlinear input space following is the formula for polynomial kernelk(xxi sum( xi)^ here is the degree of polynomialwhich we need to specify manually in the learning algorithm radial basis function (rbfkernel rbf kernelmostly used in svm classificationmaps input space in indefinite dimensional space following formula explains it mathematicallyk( ,xiexp(-gamma sum(( xi^ )