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Generate a tuning grid

The standard hyperparameters of most models provide a good fit for most datasets. Yet, they need optimization for the best performance. Otherwise, it's like driving a car with an activated hand brake. Release the brake and tune your models!

In this exercise, you'll create two objects that serve as a starting point: a tuning grid (a set of hyperparameter combinations) and a model specification that you later train with every value of the grid.

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.

# Create a specification with tuning placeholders
tune_spec <- decision_tree(___ = ___,
                           ___ = ___) %>% 
  # Specify mode
  ___ %>% 
  # Specify engine
  ___

print(tune_spec)
Edit and Run Code