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Exercise

Build your own detection model

Let's combine the tools we've seen in this chapter. The credit transfer dataset from the previous exercises was split in a training set and a test set with the same class imbalance. Next, SMOTE was applied on the training set. You'll build a classification tree model on both the original imbalanced training set and the re-balanced training set. Finally, both models will be compared on the same test set.

The rpart and caret libraries are already loaded in your workspace. Don't hesitate to refer to the slides to complete this exercise.

Instructions 1/4
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  • Train classification tree models model_orig and model_smote with rpart on the original training set train_original and SMOTE-rebalanced training set train_oversampled, respectively. Use Class as the response variable.
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