Learn how to tune hyperparameters for random forest models using tidymodels in this comprehensive tutorial. Explore #TidyTuesday data on San Francisco trees to build, preprocess, and optimize a predictive model. Discover techniques for data visualization, feature engineering, and model evaluation. Master parallel processing for efficient tuning, interpret results through informative plots, and implement various grid search strategies. Finalize the model specification, assess variable importance, and evaluate performance on test data. Gain practical insights into advanced machine learning workflows using R and tidymodels.