Data Science, Kernel Trick, Nonlinear Models, Machine Learning, Data Transformation.
solving linear problems, the model applied in feature-space must definitely underfit, resulting in a low accuracy score. This means, if the second model achieves a very high train accuracy, the problem must be linearly solvable in kernel-space. The linear regression model in feature space achieves