In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("nearest_neighbor", "kknn")

Tuning Parameters

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defaults <- 
  tibble::tibble(parsnip = c("neighbors", "weight_func", "dist_power"),
                 default = c("5L", "'optimal'", "2.0"))

param <-
  nearest_neighbor() |> 
  set_engine("kknn") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

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param$item

Parsnip changes the default range for neighbors to c(1, 15) and dist_power to c(1/10, 2).

Translation from parsnip to the original package (regression)

#| label: kknn-reg
nearest_neighbor(
  neighbors = integer(1),
  weight_func = character(1),
  dist_power = double(1)
) |>  
  set_engine("kknn") |> 
  set_mode("regression") |> 
  translate()

min_rows() will adjust the number of neighbors if the chosen value if it is not consistent with the actual data dimensions.

Translation from parsnip to the original package (classification)

#| label: kknn-cls
nearest_neighbor(
  neighbors = integer(1),
  weight_func = character(1),
  dist_power = double(1)
) |> 
  set_engine("kknn") |> 
  set_mode("classification") |> 
  translate()

Preprocessing requirements

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Examples

The "Fitting and Predicting with parsnip" article contains examples for nearest_neighbor() with the "kknn" engine.

Case weights

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Saving fitted model objects

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References

Hechenbichler K. and Schliep K.P. (2004) Weighted k-Nearest-Neighbor Techniques and Ordinal Classification, Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.