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") )
numer of neigbors considered.
Minkowski distance parameter.
logical indicating whether to scale predictors to have equal standard deviations.
kernel to use.
* excluded from grids by default
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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