Plain recipe
Using the attrition_num
dataset with all numerical data about employees who have left the company, you want to build a model that can predict if an employee is likely to stay, using Attrition
, a binary variable coded as a factor
. To get started, you will define a plain recipe that does nothing other than define the model formula and the training data.
The attrition_num
data, the logistic regression lr_model
, the user-defined class-evaluate()
function, and the train
and test
splits are loaded for you.
This exercise is part of the course
Feature Engineering in R
Exercise instructions
- Create a plain recipe defining only the model formula.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a plain recipe defining only the model formula
lr_recipe_plain <-
___(___ ~., ___ = ___)
lr_workflow_plain <- workflow() %>%
add_model(lr_model) %>%
add_recipe(lr_recipe_plain)
lr_fit_plain <- lr_workflow_plain %>%
fit(train)
lr_aug_plain <-
lr_fit_plain %>% augment(test)
lr_aug_plain %>% class_evaluate(truth = Attrition,
estimate = .pred_class,.pred_No)