View source: R/nearest_neighbor.R
nearest_neighbor | R Documentation |
nearest_neighbor()
defines a model that uses the K
most similar data
points from the training set to predict new samples. This function can
fit classification and regression models.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
nearest_neighbor(
mode = "unknown",
engine = "kknn",
neighbors = NULL,
weight_func = NULL,
dist_power = NULL
)
mode |
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". |
engine |
A single character string specifying what computational engine to use for fitting. |
neighbors |
A single integer for the number of neighbors
to consider (often called |
weight_func |
A single character for the type of kernel function used
to weight distances between samples. Valid choices are: |
dist_power |
A single number for the parameter used in calculating Minkowski distance. |
This function only defines what type of model is being fit. Once an engine
is specified, the method to fit the model is also defined. See
set_engine()
for more on setting the engine, including how to set engine
arguments.
The model is not trained or fit until the fit()
function is used
with the data.
Each of the arguments in this function other than mode
and engine
are
captured as quosures. To pass values
programmatically, use the injection operator like so:
value <- 1 nearest_neighbor(argument = !!value)
https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models
show_engines("nearest_neighbor")
nearest_neighbor(neighbors = 11)
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