When we have linearly inseparable datasets and we are using machine learning algorithms such as SVMs, we use kernels to implicitly map datapoints into a feature space that makes them linearly separable.
But how do we know if a kernel has indeed, implicitly, been successful in making the datapoints linearly separable in the new feature space? What is the guarantee?