Tidymodels 2.0: The Future of ML in R

The tidymodels ecosystem has received its most significant update ever with version 2.0. This release brings faster model training, new algorithms, and seamless integration with modern machine learning frameworks—all while maintaining the tidy principles R users love.

What's New in Tidymodels 2.0

1. Dramatically Faster Training

Tidymodels 2.0 introduces parallel processing by default, resulting in up to 5x faster model training on multi-core systems:

# Automatic parallel processing
library(tidymodels)

# This now uses all available cores automatically
model_fit <- workflow() %>%
  add_recipe(recipe) %>%
  add_model(rand_forest()) %>%
  fit_resamples(resamples = cv_folds)

2. New Algorithm Support

The parsnip package now includes native support for:

XGBoost 2.0 with GPU acceleration
LightGBM for gradient boosting
CatBoost for categorical features
TabNet for deep learning on tabular data

# Using LightGBM with tidymodels
lgbm_spec <- boost_tree(
  trees = 1000,
  tree_depth = tune(),
  learn_rate = tune()
) %>%
  set_engine("lightgbm") %>%
  set_mode("classification")

3. Improved Feature Engineering

The recipes package gains powerful new steps:

# New recipe steps in 2.0
recipe(outcome ~ ., data = training) %>%
  step_embed(all_nominal_predictors()) %>%  # Neural network embeddings
  step_time_features(date_col) %>%           # Automatic time features
  step_text_hash(text_col, num_terms = 256)  # Text hashing

4. AutoML Integration

Tidymodels 2.0 introduces auto_ml() for automated machine learning:

# Automated machine learning
auto_results <- auto_ml(
  data = training,
  outcome = "target",
  time_budget = 3600,  # 1 hour
  metric = "roc_auc"
)

# Get the best model
best_model <- auto_results %>% extract_best_model()

How Rflow Enhances Tidymodels

Rflow's AI assistant now has deep knowledge of tidymodels 2.0:

library(rflow)

# Ask Rflow to build a complete ML pipeline
rflow_ask("Create a tidymodels workflow for predicting customer churn 
           with hyperparameter tuning and cross-validation")

# Get explanations for model results
rflow_ask("Explain why this random forest model has high variable importance 
           for the 'tenure' feature")

Migration Guide

Upgrading from tidymodels 1.x? Here's what you need to know:

Update all tidymodels packages: tidymodels_update()
Review deprecated functions in the changelog
Test existing workflows—most should work unchanged
Enable parallel processing with doParallel for best performance

Conclusion

Tidymodels 2.0 solidifies R's position as a premier language for machine learning. With faster training, more algorithms, and AutoML capabilities, it's never been easier to build production-ready ML models in R.

Ready to upgrade? Run install.packages("tidymodels") to get started!