I understood that
K-Nearest-Neighbors) was non-parametric, after reading the beginning of the wikipedia article here:
In pattern recognition, the $k$-Nearest Neighbors algorithm (or $k$-NN for short) is a non-parametric method used for...
But, then later in the article it talks of estimating the "parameters"??
The best choice of $k$ depends upon the data; generally, larger values of $k$ reduce the effect of noise on the classification, but make boundaries between classes less distinct.
Am I missing the difference between parameters and hyperparameters? Thanks.