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  3. Machine Learning with Tree-Based Models in R

Exercise

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.

Instructions 1/3

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  • Extract the best-performing parameters of tune_results and save them as final_params.