tidymodels

name	tidymodels
description	R machine learning with tidymodels: recipes (preprocessing), parsnip (model specs), workflows (fit pipelines), tune (hyperparameters), rsample (resampling). Engines: ranger (RF), glmnet (lasso/ridge), xgboost, kknn. Model interpretation (permutation importance, PDP/ICE, SHAP via DALEX/iml/kernelshap) and fairness assessment (fairmodels). UMAP via uwot. Use when execution language is R. Python equivalent: scikit-learn. For econometric regression use fixest or r-stats.
autoload	never
metadata	{"audience":"research-coders","domain":"r-library","library-version":"tidymodels 1.4.1","skill-last-updated":"2026-07-07","tags":["r","machine-learning","tidymodels","classification","prediction"]}

tidymodels Skill

R machine learning with the tidymodels ecosystem. Covers recipes for preprocessing (normalization, dummy encoding, imputation, interactions), parsnip for model specifications (linear_reg, logistic_reg, rand_forest, boost_tree, nearest_neighbor), workflows for combining recipe + model into a single fit/predict pipeline, tune for hyperparameter optimization via grid search and cross-validation, rsample for resampling (v-fold CV, bootstrap, LOO), and yardstick for evaluation metrics (rmse, accuracy, roc_auc, confusion matrix). Engines: ranger (random forest), glmnet (lasso/ridge/elasticnet), xgboost (gradient boosting), kknn (k-nearest neighbors). Also covers model interpretation (permutation importance, PDP/ICE, SHAP via DALEX, iml, and kernelshap) and fairness assessment (fairmodels). UMAP via uwot (not part of tidymodels). Use when execution language is R and the task involves classification, prediction, clustering via recipes, or preprocessing for ML. Python equivalent: scikit-learn. For econometric regression (OLS, FE, IV, DiD) use fixest or r-stats.

Comprehensive skill for machine learning in R with tidymodels. Covers supervised methods (classification, regression), preprocessing (recipes), hyperparameter tuning, resampling, evaluation, and unsupervised dimension reduction. Use decision trees below to find the right guidance, then load detailed references.

What is tidymodels?

tidymodels (Kuhn & Wickham) is R's unified machine learning framework, designed as the successor to caret:

• Recipe/workflow paradigm: Preprocessing (recipe) and model (parsnip spec) are separate objects combined into a workflow -- different from scikit-learn's Pipeline where steps are positional
• Engine abstraction: set_engine() decouples model type from implementation (e.g., rand_forest() |> set_engine("ranger") vs set_engine("randomForest"))
• Tidy evaluation: Results come back as tibbles, not custom S3/S4 objects
• Consistent interface: Every model uses fit(), predict(), augment() -- same verbs regardless of engine
• Resampling first: Cross-validation and bootstrap are built into the tuning workflow, not bolted on

Version Notes

This skill targets tidymodels 1.4.1 (R 4.5.3). Key package versions:

Package	Version	Role
recipes	1.3.2	Preprocessing step definitions
parsnip	1.5.0	Model specifications + engine bindings
workflows	1.3.0	Recipe + model bundling
tune	2.0.1	Hyperparameter tuning infrastructure
rsample	1.3.2	Resampling (CV, bootstrap, etc.)
yardstick	1.4.0	Evaluation metrics
dials	1.4.3	Parameter ranges for tuning
ranger	0.18.0	Random forest engine
glmnet	4.1-10	Regularized regression engine
xgboost	3.2.1.1	Gradient boosting engine
kknn	1.4.1	K-nearest neighbors engine
uwot	0.2.4	UMAP (not tidymodels, standalone)
vip	0.4.5	Variable importance plots
DALEX	2.5.3	Model interpretation framework
iml	0.11.4	Model interpretation framework
kernelshap	0.9.1	Kernel SHAP
fairmodels	1.2.2	Fairness assessment

How to Use This Skill

Reference File Structure

Each topic in ./references/ contains focused documentation:

File	Purpose	When to Read
quickstart.md	Full tidymodels workflow: recipe, model spec, workflow, fit, predict	First use of tidymodels
recipes.md	step_normalize, step_dummy, step_impute_*, step_interact, roles	Preprocessing tasks
models.md	parsnip model specs: linear_reg, logistic_reg, rand_forest, boost_tree, nearest_neighbor	Choosing and configuring models
workflows.md	workflow() + add_recipe + add_model, fit(), predict(), augment()	Building ML pipelines
tuning.md	tune(), tune_grid(), collect_metrics(), select_best(), finalize_workflow	Hyperparameter optimization
resampling.md	vfold_cv, bootstraps, loo_cv, group_vfold_cv, strata, assessment/analysis	Creating resamples
engines.md	ranger, glmnet, xgboost, kknn: engine-specific args, installation, tuning params	Engine configuration
unsupervised.md	PCA via recipes::step_pca, UMAP via uwot::umap(), k-means via stats::kmeans	Dimension reduction, clustering
evaluation.md	yardstick metrics: rmse, accuracy, roc_auc, conf_mat, metric_set()	Evaluating model performance
interpretation.md	Permutation importance, PDP/ICE, SHAP via DALEX/iml/kernelshap, vip, native engine importance	Explaining model predictions
fairness.md	fairness_check() via fairmodels + DALEX, yardstick group metrics, reweight/roc_pivot mitigation	Fairness assessment of classifiers
gotchas.md	Recipe baking order, data leakage, parsnip mode, engine args, tidymodels vs caret	Debugging common mistakes

Reading Order

1. New to tidymodels? Start with quickstart.md then workflows.md
2. Need preprocessing? Read recipes.md
3. Choosing a model? Read models.md then engines.md
4. Need tuning? Read tuning.md then resampling.md
5. Evaluating results? Read evaluation.md
6. Explaining predictions or feature importance? Read interpretation.md
7. Fairness assessment? Read fairness.md
8. Unsupervised task? Read unsupervised.md
9. Coming from scikit-learn? Read quickstart.md then gotchas.md
10. Having issues? Check gotchas.md first

Related Skills

Skill	Relationship
scikit-learn	Python equivalent -- Pipeline/estimator pattern vs tidymodels recipe/workflow pattern. Load when execution language is Python.
data-scientist	Methodology guidance -- load for "when and why" behind methods
r-python-translation	Cross-language mappings for R tidymodels vs Python scikit-learn
fixest	Econometric regression with FE/IV/DiD in R. Use instead of tidymodels for causal inference.
r-stats	Base R stats for OLS/GLM without FE. Use instead of tidymodels for simple regression without regularization or tuning.
ggplot2	Visualization of model results. tidymodels objects are tidy -- pipe directly to ggplot2.

Routing guidance:

• For econometric regression (hypothesis testing, standard errors, coefficient interpretation), use fixest or r-stats -- not tidymodels
• For unsupervised methodology (when to cluster, how to validate), read exploratory-unsupervised.md in the data-scientist skill
• For simple OLS/GLM without tuning or regularization, use r-stats
• For data manipulation, use tidyverse

Quick Decision Trees

"I need to build a predictive model"

What kind of prediction?
+-- Continuous outcome (regression)
|   +-- Simple linear --> linear_reg() (./references/models.md)
|   +-- Regularized (lasso/ridge) --> linear_reg(penalty, mixture) with glmnet
|   |   (./references/models.md + ./references/engines.md)
|   +-- Tree-based --> rand_forest() or boost_tree()
|   |   (./references/models.md)
|   +-- Need to tune hyperparameters --> ./references/tuning.md
|   +-- NOTE: For econometric regression (causal inference, SEs),
|       use fixest or r-stats instead
+-- Categorical outcome (classification)
|   +-- Binary --> logistic_reg() (./references/models.md)
|   +-- Multi-class --> multinom_reg() or rand_forest()
|   |   (./references/models.md; logistic_reg() is binary-only)
|   +-- Best performance --> boost_tree() with xgboost
|   |   (./references/models.md + ./references/engines.md)
|   +-- Need tuning --> ./references/tuning.md

"I need to preprocess data"

What preprocessing?
+-- Normalize numeric features --> step_normalize() (./references/recipes.md)
+-- Create dummy variables --> step_dummy() (./references/recipes.md)
+-- Handle missing values --> step_impute_mean/median/knn (./references/recipes.md)
+-- Create interactions --> step_interact() (./references/recipes.md)
+-- PCA / dimension reduction --> step_pca() (./references/unsupervised.md)
+-- Mixed types (numeric + categorical) --> recipe with multiple steps
|   (./references/recipes.md)
+-- Feature engineering --> step_mutate(), step_log() (./references/recipes.md)

"I need to evaluate a model"

What evaluation?
+-- Regression metrics
|   +-- RMSE, MAE, R-squared --> ./references/evaluation.md
+-- Classification metrics
|   +-- Accuracy, ROC-AUC, confusion matrix --> ./references/evaluation.md
|   +-- Precision, recall, F1 --> ./references/evaluation.md
+-- Cross-validated performance
|   +-- collect_metrics() from tune_grid --> ./references/tuning.md
+-- Compare multiple models
|   +-- collect_metrics() + bind_rows --> ./references/evaluation.md

"I need to interpret a model or assess fairness"

What do I need?
+-- Feature importance (permutation, model-agnostic) --> DALEX model_parts()
|   or iml FeatureImp (./references/interpretation.md)
+-- Feature importance (engine-native, fast) --> ranger/xgboost importance + vip
|   (./references/interpretation.md; ranger needs importance= in set_engine)
+-- Feature effects (PDP / ICE) --> DALEX model_profile() or iml FeatureEffect
|   (./references/interpretation.md)
+-- SHAP values --> kernelshap (many rows) or predict_parts/Shapley (single row)
|   (./references/interpretation.md)
+-- Fairness metrics by protected group --> fairmodels fairness_check()
|   (./references/fairness.md)
+-- NOTE: methodology (when to interpret, causal caveats, fairness criteria)
    --> data-scientist skill: supervised-ml.md

"I need to tune hyperparameters"

Tuning approach?
+-- Grid search (exhaustive) --> tune_grid() (./references/tuning.md)
+-- Which parameters to tune --> tune() placeholder + dials ranges
|   (./references/tuning.md)
+-- Select best model --> select_best() + finalize_workflow()
|   (./references/tuning.md)
+-- Resampling for tuning --> vfold_cv() (./references/resampling.md)

"I need unsupervised analysis"

What unsupervised method?
+-- PCA (within a recipe) --> step_pca() (./references/unsupervised.md)
+-- UMAP --> uwot::umap() (./references/unsupervised.md)
+-- K-means clustering --> stats::kmeans() (./references/unsupervised.md)
+-- NOTE: tidymodels focuses on supervised learning. For advanced
    clustering, see data-scientist skill methodology guidance.

"Something isn't working"

Common issues?
+-- "recipe must be prepped" --> ./references/gotchas.md
+-- "set mode" error --> ./references/gotchas.md
+-- Engine-specific args not working --> ./references/gotchas.md
+-- Predictions wrong type --> ./references/gotchas.md
+-- Data leakage concerns --> ./references/gotchas.md
+-- tidymodels vs caret migration --> ./references/gotchas.md

File-First Execution in Research Workflows

Important: In DAAF research pipelines, tidymodels analyses are executed through script files, not interactively. This ensures auditability and reproducibility.

The pattern:

1. Write ML code to scripts/stage8_analysis/{step}_{task-name}.R
2. Execute via Bash with automatic output capture wrapper script
3. Validation results get automatically embedded in scripts as comments
4. If failed, create versioned copy for fixes

Closely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules. All ML scripts must follow the Inline Audit Trail (IAT) standard -- see agent_reference/INLINE_AUDIT_TRAIL.md. For ML code, document model selection rationale (why this algorithm, why these hyperparameters, what assumptions) with # INTENT:, # REASONING:, and # ASSUMES: comments.

See:

• agent_reference/WORKFLOW_PHASE4_ANALYSIS.md -- Stage 8 (Analysis & Visualization)
• agent_reference/INLINE_AUDIT_TRAIL.md -- IAT documentation standard

The examples below show tidymodels syntax. In research workflows, wrap them in scripts following the file-first pattern.

---

Quick Reference

Essential Setup

library(tidymodels)       # Loads recipes, parsnip, workflows, tune, rsample, yardstick, dials
library(arrow)            # For parquet I/O (DAAF convention)

The tidymodels Workflow (5 Steps)

# 1. Define recipe (preprocessing)
rec <- recipe(outcome ~ ., data = train_data) |>
  step_normalize(all_numeric_predictors()) |>
  step_dummy(all_nominal_predictors())

# 2. Define model specification
spec <- rand_forest(trees = 500) |>
  set_engine("ranger") |>
  set_mode("classification")

# 3. Bundle into workflow
wf <- workflow() |>
  add_recipe(rec) |>
  add_model(spec)

# 4. Fit
fit <- wf |> fit(data = train_data)

# 5. Predict
preds <- fit |> predict(new_data = test_data)

Common Operations

Operation	Code
Create recipe	recipe(y ~ ., data = df)
Normalize numeric	step_normalize(all_numeric_predictors())
Dummy encode	step_dummy(all_nominal_predictors())
Impute missing	step_impute_median(all_numeric_predictors())
Random forest	rand_forest(trees = 500) |> set_engine("ranger")
Logistic regression	logistic_reg() |> set_engine("glm")
Lasso	linear_reg(penalty = 0.01, mixture = 1) |> set_engine("glmnet")
Ridge	linear_reg(penalty = 0.01, mixture = 0) |> set_engine("glmnet")
XGBoost	boost_tree(trees = 500) |> set_engine("xgboost")
Build workflow	workflow() |> add_recipe(rec) |> add_model(spec)
Fit workflow	wf |> fit(data = train_data)
Predict	fit |> predict(new_data = test_data)
Augment	fit |> augment(new_data = test_data)
V-fold CV	vfold_cv(train_data, v = 10, strata = outcome)
Tune grid	tune_grid(wf, resamples = folds, grid = 20)
Best params	select_best(tune_results, metric = "rmse")
Finalize	finalize_workflow(wf, best_params)
RMSE	rmse(results, truth = y, estimate = .pred)
Accuracy	accuracy(results, truth = y, estimate = .pred_class)
ROC AUC	roc_auc(results, truth = y, .pred_class1)
Confusion matrix	conf_mat(results, truth = y, estimate = .pred_class)

Topic Index

Topic	Reference File
First tidymodels workflow	./references/quickstart.md
recipe + spec + workflow pattern	./references/quickstart.md
fit() and predict()	./references/quickstart.md
train/test split	./references/quickstart.md
step_normalize	./references/recipes.md
step_dummy	./references/recipes.md
step_impute_mean / median / knn	./references/recipes.md
step_interact	./references/recipes.md
step_mutate / step_log	./references/recipes.md
Role assignment (update_role)	./references/recipes.md
prep() and bake()	./references/recipes.md
Selector functions	./references/recipes.md
linear_reg	./references/models.md
logistic_reg	./references/models.md
multinom_reg (multiclass)	./references/models.md
rand_forest	./references/models.md
boost_tree	./references/models.md
nearest_neighbor	./references/models.md
set_engine()	./references/models.md
set_mode()	./references/models.md
workflow() construction	./references/workflows.md
add_recipe / add_model	./references/workflows.md
fit() on workflow	./references/workflows.md
predict() on workflow

Citation

When this framework is used as a primary analytical tool, include in the report's Software & Tools references:

Kuhn, M. & Wickham, H. (2020). Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org

Cite when: tidymodels is used for model fitting, preprocessing, tuning, or evaluation central to the analysis. Do not cite when: Only loaded but no modeling performed.

For engine-specific citations:

Wright, M.N. & Ziegler, A. (2017). "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R." Journal of Statistical Software, 77(1), 1-17. (ranger engine)

Friedman, J., Hastie, T., & Tibshirani, R. (2010). "Regularization Paths for Generalized Linear Models via Coordinate Descent." Journal of Statistical Software, 33(1), 1-22. (glmnet engine)

Chen, T. & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD, 785-794. (xgboost engine)

For method-specific citations, consult the reference files in this skill and agent_reference/CITATION_REFERENCE.md.