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.
There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.
kknn¹
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
Usage
nearest_neighbor(
mode = "unknown",
engine = "kknn",
neighbors = NULL,
weight_func = NULL,
dist_power = NULL
)
Arguments
- 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
k
). For kknn, a value of 5 is used ifneighbors
is not specified.- weight_func
A single character for the type of kernel function used to weight distances between samples. Valid choices are:
"rectangular"
,"triangular"
,"epanechnikov"
,"biweight"
,"triweight"
,"cos"
,"inv"
,"gaussian"
,"rank"
, or"optimal"
.- dist_power
A single number for the parameter used in calculating Minkowski distance.
Details
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)
Examples
show_engines("nearest_neighbor")
#> # A tibble: 2 × 2
#> engine mode
#> <chr> <chr>
#> 1 kknn classification
#> 2 kknn regression
nearest_neighbor(neighbors = 11)
#> K-Nearest Neighbor Model Specification (unknown mode)
#>
#> Main Arguments:
#> neighbors = 11
#>
#> Computational engine: kknn
#>