MachineShop (version 2.8.0)

KNNModel: Weighted k-Nearest Neighbor Model

Description

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.

Usage

KNNModel(
  k = 7,
  distance = 2,
  scale = TRUE,
  kernel = c("optimal", "biweight", "cos", "epanechnikov", "gaussian", "inv", "rank",
    "rectangular", "triangular", "triweight")
)

Arguments

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.

Value

MLModel class object.

Details

Response Types:

factor, numeric, ordinal

Automatic Tuning of Grid Parameters:

k, distance*, kernel*

* included only in randomly sampled grid points

Further model details can be found in the source link below.

See Also

kknn, fit, resample

Examples

# NOT RUN {
## Requires prior installation of suggested package kknn to run

fit(Species ~ ., data = iris, model = KNNModel)
# }
# NOT RUN {
# }