This is a simple classifier that bases its decision on the distances between the training dataset samples and the test sample(s). Distances are computed using a customizable distance function. A certain number (k)of nearest neighbors is selected based on the smallest distances and the labels of this neighboring samples are fed into a voting function to determine the labels of the test sample.
Training a kNN classifier is extremely quick, as no actual training is performed as the training dataset is simply stored in the classifier. All computations are done during classifier prediction.
If enabled, kNN stores the votes per class in the ‘values’ state after calling predict().
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
k : unsigned integer
dfx : functor
voting : str
enable_ca : None or list of str
disable_ca : None or list of str
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str