knnClassif: K-Nearest Neighbors Classification

In amanda-park/easytidymodels: Make tidymodels even easier to run!

Description

Fits a K-Nearest Neighbors Classification Model.

Usage

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

Arguments

response	Character. The variable that is the response for analysis.
recipe	A 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 classification metric you want to evaluate the model's accuracy on. Default is bal_accuracy. List of metrics available to choose from: bal_accuracy mn_log_loss roc_auc mcc kap sens spec precision recall

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.

Value

A list with the following outputs:

• Training confusion matrix

• Training model metric score

• Testing confusion matrix

• Testing model metric score

• Final model chosen

• Tuned model

Examples

library(easytidymodels)
library(dplyr)
library(recipes)
utils::data(penguins, package = "modeldata")
#Define your response variable and formula object here
resp <- "sex"
formula <- stats::as.formula(paste(resp, ".", sep="~"))
#Split data into training and testing sets
split <- trainTestSplit(penguins, stratifyOnResponse = TRUE,
responseVar = resp)
#Create recipe for feature engineering for dataset, varies based on data working with
rec <- recipe(formula, data = split$train) %>% step_knnimpute(!!resp) %>%
step_dummy(all_nominal(), -all_outcomes()) %>%
step_medianimpute(all_predictors()) %>% step_normalize(all_predictors()) %>%
step_dummy(all_nominal(), -all_outcomes()) %>% step_nzv(all_predictors()) %>%
step_corr(all_numeric(), -all_outcomes(), threshold = .8) %>% prep()
train_df <- bake(rec, split$train)
test_df <- bake(rec, split$test)
folds <- cvFolds(train_df)

#knn <- svmClassif(recipe = rec, response = resp, folds = folds,
#train = train_df, test = test_df)

#Confusion Matrix
#knn$trainConfMat

#Plot of confusion matrix
#knn$trainConfMatPlot

#Test Confusion Matrix
#knn$testConfMat

#Test Confusion Matrix Plot
#knn$testConfMatPlot

amanda-park/easytidymodels documentation built on Dec. 13, 2021, 11:28 a.m.