Definition of "condensed nearest neighbour", at training time it chooses the c "best" training examples (where c is a hyper-parameter), and at test time uses the usual KNN prediction but based only on these c training examples.
So far, I looked-up many references and websites and researched on how to determine if a method is between parametric or non-parametric. I came up with below definitions,
A parametric algorithm has a fixed number of parameters. In contrast, a non-parametric algorithm uses a flexible number of parameters, and the number of parameters often grows as it learns from more data.
Moreover, I found,
A parametric model, we have a finite number of parameters, and in nonparametric models, the number of parameters is (potentially) infinite.
From https://sebastianraschka.com/faq/docs/parametric_vs_nonparametric.html.
My question is, why do we count it as a Parametric model?
