How can I choose the best kernel for a Gaussian process regression, possibly using "bayesopt" function?
For example setting this variable:
kernel=optimizableVariable('KernelFunction',{'exponential','squaredexponential','matern32','matern52',...'rationalquadratic','ardexponential','ardsquaredexponential','ardmatern32','ardmatern52','ardrationalquadratic'},'Type','categorical')
How can I predict unseen data with the resulting model?
Best Answer