tidymodels-workflow

# 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 # Define tuning grid tune_grid <- grid_regular( mtry(range = c(2, 10)), min_n(range = c(2, 20)), levels = 5 ) # Tune model tune_results <- workflow_spec |> tune_grid( resamples = cv_folds, grid = tune_grid, metrics = metric_set(roc_auc, accuracy) ) ``` ### Model Selection ```r # Select best parameters best_params <- select_best(tune_results, metric = "roc_auc") # Finalize workflow final_workflow <- workflow_spec |> finalize_workflow(best_params) ``` ### Final Fit ```r # Fit on full training data, evaluate on test final_fit <- final_workflow |> last_fit(data_split) # Extract metrics collect_metrics(final_fit) # Extract predictions collect_predictions(final_fit) ``` ### Model Extraction and Deployment ```r # Extract fitted workflow fitted_wf <- extract_workflow(final_fit) # Save model saveRDS(fitted_wf, "output/models/final_model.rds") # Predict on new data predictions <- predict(fitted_wf, new_data) ``` ## Complete Workflow Example ```r library(tidymodels) tidymodels_prefer() # 1. Load and split data set.seed(123) data_split <- initial_split(ames, prop = 0.75, strata = Sale_Price) # 2. Create recipe ames_recipe <- recipe(Sale_Price ~ ., data = training(data_split)) |> step_log(Sale_Price, base = 10) |> step_other(Neighborhood, threshold = 0.05) |> step_dummy(all_nominal_predictors()) |> step_normalize(all_numeric_predictors()) |> step_zv(all_predictors()) # 3. Specify model xgb_spec <- boost_tree( trees = tune(), tree_depth = tune(), learn_rate = tune() ) |> set_engine("xgboost") |> set_mode("regression") # 4. Create workflow xgb_wf <- workflow(ames_recipe, xgb_spec) # 5. Setup resampling cv_folds <- vfold_cv(training(data_split), v = 5) # 6. Tune hyperparameters xgb_tune <- xgb_wf |> tune_grid( resamples = cv_folds, grid = 20, metrics = metric_set(rmse, rsq) ) # 7. Select best and finalize best_xgb <- select_best(xgb_tune, metric = "rmse") final_wf <- finalize_workflow(xgb_wf, best_xgb) # 8. Final evaluation final_fit <- last_fit(final_wf, data_split) collect_metrics(final_fit) ``` ## Workflow Sets for Model Comparison ```r # Create multiple preprocessing recipes basic_recipe <- recipe(outcome ~ ., data = train) |> step_normalize(all_numeric_predictors()) pca_recipe <- basic_recipe |> step_pca(all_numeric_predictors(), num_comp = 5) # Create multiple model specifications lm_spec <- linear_reg() |> set_engine("lm") rf_spec <- rand_forest(trees = 500) |> set_engine("ranger") |> set_mode("regression") xgb_spec <- boost_tree() |> set_engine("xgboost") |> set_mode("regression") # Create workflow set wf_set <- workflow_set( preproc = list(basic = basic_recipe, pca = pca_recipe), models = list(lm = lm_spec, rf = rf_spec, xgb = xgb_spec) ) # Fit all workflows wf_results <- wf_set |> workflow_map( resamples = cv_folds, grid = 10, verbose = TRUE ) # Compare results autoplot(wf_results) rank_results(wf_results, rank_metric = "rmse") ``` ## Key Packages - **rsample**: Data splitting and resampling - **recipes**: Feature engineering - **parsnip**: Model specification - **workflows**: Combine preprocessing and models - **tune**: Hyperparameter optimization - **yardstick**: Model evaluation metrics - **workflowsets**: Compare multiple workflows - **broom**: Tidy model outputs ## Best Practices 1. Always set a seed before splitting data 2. Use stratified sampling for imbalanced outcomes 3. Keep test data completely separate until final evaluation 4. Use cross-validation for honest performance estimates 5. Tune hyperparameters on training data only 6. Use `last_fit()` for final evaluation on test set 7. Save the complete workflow object for deployment