Multinomial Regression

Runs a multinomial regression model, evaluates it on training and testing set, and tunes hyperparameters.

logRegMulti(
  recipe = rec,
  folds = cvFolds,
  train = train_df,
  test = test_df,
  response = response,
  gridNum = 15,
  evalMetric = "bal_accuracy"
)

Arguments

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.

response

Character. The variable that is the response for analysis.

gridNum

Numeric. The number of levels you want the grid to search 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

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

Details

What the model tunes:

  • penalty: The total amount of regularization in the model. Also known as lambda.

  • mixture: The mixture amounts of different types of regularization (see below). If 1, amounts to LASSO regression. If 0, amounts to Ridge Regression. Also known as alpha.

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

#Confusion Matrix
#mr$trainConfMat

#Plot of confusion matrix
#mr$trainConfMatPlot

#Test Confusion Matrix
#mr$testConfMat

#Test Confusion Matrix Plot
#mr$testConfMatPlot