logRegBinary: Logistic Regression

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

Description

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

Usage

logRegBinary(
  recipe = rec,
  folds = cvFolds,
  train = datTrain,
  test = datTest,
  response = response,
  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.
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

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.

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)

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

#Confusion Matrix
#lr$trainConfMat

#Plot of confusion matrix
#lr$trainConfMatPlot

#Test Confusion Matrix
#lr$testConfMat

#Test Confusion Matrix Plot
#lr$testConfMatPlot

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