KNNModel {MachineShop} | R Documentation |
Fit a k-nearest neighbor model for which the k nearest training set vectors (according to Minkowski distance) are found for each row of the test set, and prediction is done via the maximum of summed kernel densities.
KNNModel( k = 7, distance = 2, scale = TRUE, kernel = c("optimal", "biweight", "cos", "epanechnikov", "gaussian", "inv", "rank", "rectangular", "triangular", "triweight") )
k |
numer of neigbors considered. |
distance |
Minkowski distance parameter. |
scale |
logical indicating whether to scale predictors to have equal standard deviations. |
kernel |
kernel to use. |
factor
, numeric
, ordinal
k
, distance
*, kernel
*
* included only in randomly sampled grid points
Further model details can be found in the source link below.
MLModel
class object.
## Requires prior installation of suggested package kknn to run fit(Species ~ ., data = iris, model = KNNModel)