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Compare AUC

Comparing different models is the core of model selection. In the final two exercises, you'll perform a model comparison across all types of models in this course: decision trees, bagged trees, random forests, and gradient boosting.

The models were all tuned to perfection and trained on the same training set, customers_train, and predictions were made for the customers_test dataset. The results are numeric probabilities and are available as preds_combined in your session:

tibble [1,011 × 5]
 $ preds_tree    : 0.144 0.441 ...
 $ preds_bagging : 0.115 0.326 ...
 $ preds_forest  : 0 0 0 0.286 ...
 $ preds_boosting: 0.136 0.149 ...
 $ still_customer: "no","no", ...

This exercise is part of the course

Machine Learning with Tree-Based Models in R

View Course

Hands-on interactive exercise

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

# Calculate the AUC for each model
auc_tree   <- ___(preds_combined, truth = ___, estimate = ___)
auc_bagged <- ___(preds_combined, truth = ___, estimate = ___)
auc_forest <- ___(preds_combined, truth = ___, estimate = ___)
auc_boost  <- ___(preds_combined, truth = ___, estimate = ___)

# Print the results
auc_tree
auc_bagged
auc_forest
auc_boost
Edit and Run Code