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Fitting and assessing the model

Now that you have addressed missing values and created dummy variables, it is time to assess your model's performance!

The attritiondataset, along with the testand train splits, the lr_recipe and your declared logistic_model() are all loaded for you.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

  • Bundle model and recipe in workflow.
  • Fit workflow to the train data.
  • Generate an augmented data frame for performance assessment.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Bundle model and recipe in workflow
lr_workflow <- ___() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe)

# Fit workflow to the train data
lr_fit <- ___(lr_workflow, data = train)

# Generate an augmented data frame for performance assessment
lr_aug <- lr_fit %>% ___(test)

lr_aug %>% roc_curve(truth = Attrition, .pred_No) %>% autoplot()
bind_rows(lr_aug %>% roc_auc(truth = Attrition, .pred_No),          
          lr_aug %>% accuracy(truth = Attrition, .pred_class))
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