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When using KNeighborsClassifier, what is the motivation of using weights="distance"?
According to the scikit-learn documentation:
‘distance’ : weight points by the inverse of their distance. In this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
What is the motivation for using this?
weights = 'distance' is in contrast to the default which is weights = 'uniform'. When weights are uniform, a simple majority vote of the nearest neighbors is used to assign cluster membership.
When weights are distance weighted, the voting is proportional to the distance value. Nearby points will have a greater influence than more distance points (even if the counts of different groups are the similar).
Distance weighting is very useful for sparse data.
