nearest-neighbor.md
In parsnip: A Common API to Modeling and Analysis Functions

Engine Details

Engines may have pre-set default arguments when executing the model fit call. For this type of model, the template of the fit calls are below:

nearest_neighbor() |> 
  set_engine("kknn") |> 
  set_mode("regression") |> 
  translate()

## K-Nearest Neighbor Model Specification (regression)
## 
## Computational engine: kknn 
## 
## Model fit template:
## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), 
##     ks = min_rows(5, data, 5))

nearest_neighbor() |> 
  set_engine("kknn") |> 
  set_mode("classification") |> 
  translate()

## K-Nearest Neighbor Model Specification (classification)
## 
## Computational engine: kknn 
## 
## Model fit template:
## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), 
##     ks = min_rows(5, data, 5))

For kknn, the underlying modeling function used is a restricted version of train.kknn() and not kknn(). It is set up in this way so that parsnip can utilize the underlying predict.train.kknn method to predict on new data. This also means that a single value of that function's kernel argument (a.k.a weight_func here) can be supplied

For this engine, tuning over neighbors is very efficient since the same model object can be used to make predictions over multiple values of neighbors.

The standardized parameter names in parsnip can be mapped to their original names in each engine that has main parameters. Each engine typically has a different default value (shown in parentheses) for each parameter.

|parsnip |kknn | |:-----------|:----------------| |neighbors |ks | |weight_func |kernel (optimal) | |dist_power |distance (2) |