Tidymodels Overview

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 
  step_normalize(all_numeric_predictors())

# This estimates parameters (means, sds) from training data
prepped 
  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 
  set_engine("ranger") |>
  set_mode("regression")

4. Bundling (workflows)

Combine preprocessing and model into a single object:

wflow 
  add_recipe(rec_spec) |>
  add_model(model_spec)

5. Evaluation (tune + yardstick)

Use resampling or validation sets to assess performance:

# Define metrics
metrics 
  step_normalize(...) |>
  prep()

Use Selectors, Not String Matching

Avoid constructing variable lists manually:

# WRONG - manual string matching
numeric_cols  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 
  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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: jsperger
• Source: jsperger/llm-r-skills
• License: MIT

Install and usage instructions live in the source repository linked above.