K-Nearest Neighbors Regression

Fits a K-Nearest Neighbors Regression Model.

knnRegress(
  response = response,
  recipe = rec,
  folds = folds,
  train = train_df,
  test = test_df,
  gridNumber = 15,
  evalMetric = "rmse"
)

Arguments

response

Character. The variable that is the response for analysis.

recipe

A recipes::recipe object.

folds

A rsample::vfolds_cv object.

train

Data frame/tibble. The training data set.

test

Data frame/tibble. The testing data set.

gridNumber

Numeric. The size of the grid to tune on. Default is 15.

evalMetric

Character. The regression metric you want to evaluate the model's accuracy on. Default is RMSE. Can choose from the following:

  • rmse

  • mae

  • rsq

  • mase

  • ccc

  • icc

  • huber_loss

Value

A list with the following elements:

  • Training set predictions

  • Training set evaluation on RMSE and MAE

  • Testing set predictions

  • Testing set evaluation on RMSE and MAE

  • Tuned model object

Details

Note: tunes the following parameters:

  • neighbors: The number of neighbors considered at each prediction.

  • weight_func: The type of kernel function that weights the distances between samples.

  • dist_power: The parameter used when calculating the Minkowski distance. This corresponds to the Manhattan distance with dist_power = 1 and the Euclidean distance with dist_power = 2.

Examples

library(easytidymodels)
library(dplyr)
library(recipes)
utils::data(penguins, package = "modeldata")

#Define your response variable and formula object here
resp <- "bill_length_mm"
formula <- stats::as.formula(paste(resp, ".", sep="~"))

#Split data into training and testing sets
split <- trainTestSplit(penguins, responseVar = resp)

#Create recipe for feature engineering for dataset, varies based on data working with
rec <- recipe(formula, split$train) %>% prep()
train_df <- bake(rec, split$train)
test_df <- bake(rec, split$test)
folds <- cvFolds(train_df)

#Fit a KNN regression object (commented out only due to long run time)
#knnReg <- knnRegress(recipe = rec, response = resp,
#folds = folds, train = train_df, test = test_df, evalMetric = "rmse")

#Visualize training data and its predictions
#knnReg$trainPred %>% select(.pred, !!resp)

#View how model metrics for RMSE, R-Squared, and MAE look for training data
#knnReg$trainScore

#Visualize testing data and its predictions
#knnReg$testPred %>% select(.pred, !!resp)

#View how model metrics for RMSE, R-Squared, and MAE look for testing data
#knnReg$testScore

#See the final model chosen by KNN based on optimizing for your chosen evaluation metric
#knnReg$final

#See how model fit looks based on another evaluation metric
#knnReg$tune %>% tune::show_best("mae")