details_nearest_neighbor_kknn: K-nearest neighbors via kknn

In parsnip: A Common API to Modeling and Analysis Functions

K-nearest neighbors via kknn

Description

kknn::train.kknn() fits a model that uses the K most similar data points from the training set to predict new samples.

Details

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

Tuning Parameters

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)

Translation from parsnip to the original package (regression)

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.

Translation from parsnip to the original package (classification)

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))

Preprocessing requirements

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(), 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.

Examples

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

Case weights

The underlying model implementation does not allow for case weights.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

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.

parsnip documentation built on June 24, 2024, 5:14 p.m.