tidymodels-workflowlisted

# tidymodels Workflow Patterns ## Overview Core workflow patterns for building machine learning models using the tidymodels ecosystem. Covers the complete pipeline from data splitting through model deployment. ## Core Workflow Components ### Data Splitting with rsample ```r library(tidymodels) # Basic train/test split set.seed(123) data_split <- initial_split(data, prop = 0.75, strata = outcome) train_data <- training(data_split) test_data <- testing(data_split) # Validation set approach data_split <- initial_validation_split(data, prop = c(0.6, 0.2)) train_data <- training(data_split) val_data <- validation(data_split) test_data <- testing(data_split) ``` ### Recipe Creation ```r # Create preprocessing recipe recipe_spec <- recipe(outcome ~ ., data = train_data) |> step_normalize(all_numeric_predictors()) |> step_dummy(all_nominal_predictors()) |> step_zv(all_predictors()) ``` ### Model Specification with parsnip ```r # Specify model with tune placeholders model_spec <- rand_forest( mtry = tune(), trees = 1000, min_n = tune() ) |> set_engine("ranger") |> set_mode("classification") ``` ### Workflow Assembly ```r # Combine recipe and model workflow_spec <- workflow() |> add_recipe(recipe_spec) |> add_model(model_spec) ``` ### Resampling Setup ```r # Cross-validation folds cv_folds <- vfold_cv(train_data, v = 10, strata = outcome) # Bootstrap samples boot_samples <- bootstraps(train_data, times = 25) ``` ### Hyperparameter Tuning ```r # Def