Model Tuning Patterns

What problem does it solve?

Streamline hyperparameter optimization for tidymodels pipelines, reducing manual trial-and-error and accelerating model deployment.

Core Features & Use Cases

• Grid search across mtry, min_n, and trees to identify strong defaults for random forests and boosted trees.
• Bayesian optimization with tune_bayes to efficiently explore promising regions of the parameter space.
• Racing and adaptive strategies such as tune_race_anova and tune_race_win_loss to quickly discard poor configurations.
• Real-world scenario: tuning a regression model to maximize roc_auc or rsq on cross-validated splits, then finalizing the best workflow.

Quick Start

Run a simple grid search on your tidymodels workflow to find the best mtry and min_n for a classification task.