`nearest_neighbor()`

defines a model that uses the `K`

most similar data
points from the training set to predict new samples.

There are different ways to fit this model. See the engine-specific pages
for more details:

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
)

## 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.

The model is not trained or fit until the `fit.model_spec()`

function is used
with the data.

## References

https://www.tidymodels.org, *Tidy Models with R*

## See also

## Examples

#> # A tibble: 2 × 2
#> engine mode
#> <chr> <chr>
#> 1 kknn classification
#> 2 kknn regression

nearest_neighbor(neighbors = 11)

#> K-Nearest Neighbor Model Specification (unknown)
#>
#> Main Arguments:
#> neighbors = 11
#>
#> Computational engine: kknn
#>