When considering more than one neighbor, we use voting to assign a label. This means that for each test point, we count how many neighbors belong to class 0 and how many neighbors belong to class 1.which ever is greatest in number that label is put on the training data point.
This is how the NNeighborClassfier classifies but if the N_neighbor parameter value is given even like 2,4,6. and same no of both class exist in the neighbor like example
N_neighbor =4
class 0 =2
class 1 =2
then what will it predict and what answer will it give
