`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 if`neighbors`

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
#>
```