Tuning preparation

Tuning preparation is the foundation for tuning success. There are two main steps in preparing your tuning: marking hyperparameters using tune() in the model specification and creating a grid of hyperparameters that is used in tuning.

You are going to execute these two fundamental steps of the tuning process in this exercise.

This exercise is part of the course

Machine Learning with Tree-Based Models in R

Exercise instructions

Create a boosting specification with an "xgboost" engine for a classification model using 500 trees and mark the following parameters as tuning parameters: learn_rate, tree_depth, and sample_size. Save the result as boost_spec.
Build a regular tuning grid for the tuning parameters of boost_spec with three levels for each parameter.

Hands-on interactive exercise

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

# Create the specification with placeholders
boost_spec <- boost_tree(
                trees = ___,
                ___,
                ___,
                ___) %>%
  set_mode(___) %>%
  set_engine(___)

# Create the tuning grid
tunegrid_boost <- ___(___, 
                      levels = ___)

tunegrid_boost

Edit and Run Code

This exercise is part of the course

Machine Learning with Tree-Based Models in R

IntermediateSkill Level

4.9+

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Current Exercise

Exercise 10: The actual tuning Exercise 11: Finalize the model Exercise 12: Model comparison Exercise 13: Compare AUC Exercise 14: Plot ROC curves Exercise 15: Wrap-up