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Double descent describes how test error first follows the classic U-shape with increasing model complexity, spikes near the interpolation threshold, and then drops again in the highly overparameterized regime.
An RKHS is a space of functions where evaluating a function at a point equals taking an inner product with a kernel section, which enables the “kernel trick.”