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Pick the winner

Once tuning has been performed, it's time to pick the optimal hyperparameters from the results and build the final model. Two helpers from tidymodels come in handy:

The function select_best() extracts the optimal hyperparameters from a tuning results tibble, and finalize_model() plugs these results into the specification, replacing the placeholders.

It's your turn to try this using the results of the last exercise! The objects tune_spec, tune_results, and customers are still loaded.

This exercise is part of the course

Machine Learning with Tree-Based Models in R

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Hands-on interactive exercise

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

# Select the parameters that perform best
final_params <- ___

final_params
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