I know the Nadaraya-Watson kernel regression. What is new to me is the Kernel ridge regression from scitkit-learn's KernelRidge with kernel='rbf'. It mentions that the kernel trick is used, which is not done in the Nadaraya-Watson kernel regression, so it seems to me they are different concepts. Am I right, or are they the same afterall?
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Yeah, you are right. You practically replace the square matrix $X^TX$ with a Kernel $K$ when you estimate your coefficients.
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