Adjust model complexity
To make good predictions, you need to adjust the complexity of your model. Simple models can only represent simple data structures, while complex models can represent fine-grained data structures.
In this exercise, you are going to create trees of different complexities by altering the hyperparameters of a regression tree.
The training data chocolate_train is pre-loaded in your workspace.
Deze oefening maakt deel uit van de cursus
Machine Learning with Tree-Based Models in R
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Create a model having only one split
chocolate_model <- ___(___) %>%
set_mode("regression") %>%
set_engine("rpart") %>%
fit(final_grade ~ ., data = chocolate_train)
chocolate_model