knnRegress: K-Nearest Neighbors Regression
In amanda-park/easytidymodels: Make tidymodels even easier to run!

Description Usage Arguments Details Value Examples

Fits a K-Nearest Neighbors Regression Model.

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

`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

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.

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

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")