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I have a question regarding the Condensed Nearest Neighbors algorithm:
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Why am I returning Z, which if I understand correctly, is the array of all of the misclassified points? Wouldn't I want to return the points that were classified correctly? What benefit does this give me in returning all the points I got wrong?

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Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.

It is returning not the array of misclassified points, but a subset Z of the data set X.

CNN works like that:

1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x

2) Remove x from X and add it to Z

3) Repeat the scan until no more prototypes are added to Z

Z used instead of X for kNN classification.

An advantage of this method is decreasing of execution time, reducing a space complexity

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