Weighted k-Nearest Neighbor Model
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)
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