# hyperparameters optimisation with linear kernel

I want to conduct an SVM model-regression (i.e., support vector regression), using a linear kernel function. Does it make sense to perform a cross-validation hyperparameter optimization when the kernel function is linear? If so, what should be the range of values for each hyperparameter in the search?

Thank you

If anything, the choosing the regularisation parameter $$C$$ in the Lagrange formulation is analogous to choosing the ridge regularisation parameter in ridge regression. Therefore it is necessary for our training data to be scaled appropriately (usually to mean $$0$$ and st.dev. $$1$$). Regarding the actual choice of the $$C$$, it is generally common to use exponentially growing sequences of $$C$$; e.g. $$C = 2^{-6},2^{-5},\ldots,2^{5},2^{6}$$, etc. CV.SE has a nice thread on this matter if one wants to explore this further: "Which search range for determining SVM optimal C and gamma parameters?".