tidymodels-overview

name

tidymodels-overview

description

This skill should be used when working with R tidymodels packages, including when the user asks to "create a tidymodels workflow", "build a recipe", "tune a model", "use parsnip", "set up resampling", "create a workflow_set", "compare models", "stack models", or mentions tidymodels packages like recipes, parsnip, workflows, workflowsets, tune, rsample, yardstick, or stacks. Provides ecosystem context before package-specific skills.

Tidymodels Overview

The tidymodels ecosystem provides a consistent, modular framework for machine learning in R. Understanding the ecosystem context helps when working with any tidymodels pipeline before diving into package-specific details.

Core Principle: Recipes Are Plans, Not Actions

Critical: A recipe object is a specification of preprocessing steps. Adding steps like step_normalize() does not transform data immediately. Transformations execute only when:

1. prep() estimates parameters from training data
2. bake() applies the prepped recipe to new data

# This does NOT transform data - it creates a plan
rec <- recipe(outcome ~ ., data = train) |>
  step_normalize(all_numeric_predictors())

# This estimates parameters (means, sds) from training data
prepped <- prep(rec, training = train)

# This applies transformations to new data
processed <- bake(prepped, new_data = test)

The Tidymodels Workflow

Follow this standard workflow for modeling projects:

1. Data Splitting (rsample)

Allocate data to training, validation, and test sets before any modeling:

set.seed(123)
data_split <- initial_split(data, prop = 0.8, strata = outcome)
train_data <- training(data_split)
test_data  <- testing(data_split)

# For iterative evaluation during development
resamples <- vfold_cv(train_data, v = 10)

2. Preprocessing (recipes)

Define feature engineering as a recipe specification:

rec_spec <- recipe(outcome ~ ., data = train_data) |>
  step_normalize(all_numeric_predictors()) |>
  step_dummy(all_factor_predictors()) |>
  step_zv(all_predictors())

Use tidyselect helpers for column selection:

• all_predictors(), all_outcomes() - by role
• all_numeric_predictors(), all_nominal_predictors() - by type and role
• has_role(), has_type() - explicit queries

3. Model Specification (parsnip)

Define the model type, engine, and mode separately from fitting:

model_spec <- rand_forest(mtry = tune(), trees = 1000) |>
  set_engine("ranger") |>
  set_mode("regression")

4. Bundling (workflows)

Combine preprocessing and model into a single object:

wflow <- workflow() |>
  add_recipe(rec_spec) |>
  add_model(model_spec)

5. Evaluation (tune + yardstick)

Use resampling or validation sets to assess performance:

# Define metrics
metrics <- metric_set(rmse, rsq, mae)

# Tune hyperparameters
tuned <- tune_grid(
  wflow,
  resamples = resamples,
  grid = 10,
  metrics = metrics
)

# Select best parameters
best_params <- select_best(tuned, metric = "rmse")

6. Finalization

Finalize the workflow and fit to full training data:

final_wflow <- finalize_workflow(wflow, best_params)
final_fit <- last_fit(final_wflow, split = data_split)

# Extract test set metrics
collect_metrics(final_fit)

Package Roles

Package	Purpose	Key Functions
rsample	Data splitting and resampling	initial_split(), vfold_cv(), bootstraps()
recipes	Preprocessing specification	recipe(), step_*(), prep(), bake()
parsnip	Model specification	Model functions, set_engine(), set_mode()
workflows	Bundle recipe + model	workflow(), add_recipe(), add_model()
tune	Hyperparameter optimization	tune_grid(), tune_bayes(), select_best()
yardstick	Performance metrics	metric_set(), rmse(), accuracy()
workflowsets	Compare multiple pipelines	workflow_set(), workflow_map()
stacks	Model ensembling	stacks(), add_candidates(), blend_predictions()
hardhat	Internal infrastructure	mold(), forge(), blueprints

Key Principles

Use Package Functions, Not Direct Access

Never directly modify tidymodels object internals. Always use provided functions:

# WRONG - directly modifying internals
recipe_obj$steps[[1]]$means <- new_means

# CORRECT - use proper functions
rec <- recipe(...) |>
  step_normalize(...) |>
  prep()

Use Selectors, Not String Matching

Avoid constructing variable lists manually:

# WRONG - manual string matching
numeric_cols <- names(data)[sapply(data, is.numeric)]
rec |> step_normalize(all_of(numeric_cols))

# CORRECT - use tidyselect helpers
rec |> step_normalize(all_numeric_predictors())

Understand Role Requirements

Custom roles are required at bake() time by default. When using step_rm() with custom roles, update requirements:

rec <- recipe(...) |>
  update_role(id_column, new_role = "id") |>
  update_role_requirements("id", bake = FALSE) |>
  step_rm(has_role("id"))

workflowsets Require Same Outcome

All workflows in a workflow_set must predict the same outcome variable. For different outcomes, create separate workflow sets.

When to Use Each Package

• Simple model: recipes + parsnip + workflows
• Hyperparameter tuning: Add tune
• Model comparison: Add workflowsets
• Ensemble models: Add stacks (requires save_pred = TRUE, save_workflow = TRUE)
• Custom preprocessing interfaces: Use hardhat

Additional Resources

Reference Files

For detailed information, consult:

• references/packages.md - Detailed package documentation including object structures, creation processes, and deep knowledge links
• references/common-problems.md - Common pitfalls when working with tidymodels and how to avoid them

External Documentation

• tidymodels.org - Official documentation and tutorials
• recipes.tidymodels.org - Recipe step reference
• parsnip.tidymodels.org - Model specifications