features the kernel trick the lesson here is that adding nonlinear features to the representation of our data can make linear models much more powerful howeveroften we don' know which features to addand adding many features (like all possible interactions in dimensional feature spacemight make computation very expensive luckilythere is clever mathematical trick that allows us to learn classifier in higher-dimensional space without actually computing the newpossibly very large representation this is known as the kernel trickand it works by directly computing the distance (more preciselythe scalar productsof the data points for the expanded feature representationwithout ever actually computing the expansion there are two ways to map your data into higher-dimensional space that are commonly used with support vector machinesthe polynomial kernelwhich computes all possible polynomials up to certain degree of the original features (like feature * feature * )and the radial basis function (rbfkernelalso known as the gaussian kernel the gaussian kernel is bit harder to explainas it corresponds to an infinite-dimensional feature space one way to explain the gaussian kernel is that supervised machine learning algorithms