`kknn::train.kknn()`

fits a model that uses the `K`

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

For this engine, there are multiple modes: classification and regression

This model has 3 tuning parameters:

`neighbors`

: # Nearest Neighbors (type: integer, default: 5L)`weight_func`

: Distance Weighting Function (type: character, default: ‘optimal’)`dist_power`

: Minkowski Distance Order (type: double, default: 2.0)

nearest_neighbor( neighbors = integer(1), weight_func = character(1), dist_power = double(1) ) %>% set_engine("kknn") %>% set_mode("regression") %>% translate()

## K-Nearest Neighbor Model Specification (regression) ## ## Main Arguments: ## neighbors = integer(1) ## weight_func = character(1) ## dist_power = double(1) ## ## Computational engine: kknn ## ## Model fit template: ## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), ## ks = min_rows(0L, data, 5), kernel = character(1), distance = double(1))

`min_rows()`

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

nearest_neighbor( neighbors = integer(1), weight_func = character(1), dist_power = double(1) ) %>% set_engine("kknn") %>% set_mode("classification") %>% translate()

## K-Nearest Neighbor Model Specification (classification) ## ## Main Arguments: ## neighbors = integer(1) ## weight_func = character(1) ## dist_power = double(1) ## ## Computational engine: kknn ## ## Model fit template: ## kknn::train.kknn(formula = missing_arg(), data = missing_arg(), ## ks = min_rows(0L, data, 5), kernel = character(1), distance = double(1))

Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via
`fit.model_spec()`

, parsnip will
convert factor columns to indicators.

Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.

The “Fitting and Predicting with parsnip” article contains
examples
for `nearest_neighbor()`

with the `"kknn"`

engine.

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.