Het model fitten en beoordelen

Nu je ontbrekende waarden hebt aangepakt en dummyvariabelen hebt gemaakt, is het tijd om de prestaties van je model te beoordelen!

De attrition-gegevensset, samen met de test- en train-splits, het lr_recipe en je gedeclareerde logistic_model() zijn allemaal voor je geladen.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

Cursus bekijken

Oefeninstructies

Bundel model en recipe in een workflow.
Fit de workflow op de trainingsdata.
Genereer een augmented data frame voor prestatiebeoordeling.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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))

Code bewerken en uitvoeren

Feature engineering in R

SkillTag.level.intermediateSkillTag.label

4.8+

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You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!