tidyAML

Automated Machine Learning with tidymodels - Build and compare multiple ML models effortlessly

Overview

To view the full wiki, click here: Full tidyAML Wiki

{tidyAML} is an R package that brings the power of Automated Machine Learning (AutoML) to the tidymodels ecosystem. With just a few lines of code, you can generate, train, and compare multiple machine learning models simultaneously, making it perfect for both rapid prototyping and production workflows.

Key Features

🚀 Fast Model Generation: Create multiple model specifications instantly
🔄 Batch Training: Train dozens of models with a single function call
📊 Both Regression & Classification: Support for all common ML tasks
🛡️ Graceful Failure Handling: Models fail safely without breaking your workflow
🎯 tidymodels Native: Built on the robust tidymodels framework
⚡ No Java Required: Unlike h2o, runs purely in R
🔌 Extensible: Works with 30+ parsnip engines out of the box

Why tidyAML?

Feature	tidyAML	h2o	caret
tidymodels Integration	✅ Native	❌ No	⚠️ Limited
Java Required	✅ No	❌ Yes	✅ No
Parallel Model Training	✅ Yes	✅ Yes	✅ Yes
Modern R Workflow	✅ Pipes & tidy	❌ Old style	⚠️ Mixed
Active Development	✅ Yes	⚠️ Slowing	❌ Maintenance

Installation
Quick Start
Regression Example
Classification Example
Key Functions
Visualization
Documentation
Contributing
Citation

Installation

Install the stable version from CRAN:

install.packages("tidyAML")

Or get the development version from GitHub:

# install.packages("devtools")
devtools::install_github("spsanderson/tidyAML")

After installation, it’s recommended to set tidymodels preferences:

library(tidyAML)
tidymodels::tidymodels_prefer()

Quick Start

Here’s a minimal example to get you started:

library(tidyAML)
library(recipes)

# Prepare a recipe
rec_obj <- recipe(mpg ~ ., data = mtcars)

# Generate and train multiple models at once
models <- fast_regression(
  .data = mtcars,
  .rec_obj = rec_obj,
  .parsnip_eng = c("lm", "glm", "glmnet")
)

# Extract predictions
extract_wflw_pred(models, 1:3)

Regression Example

Let’s build multiple regression models to predict car mileage (mpg) using the mtcars dataset:

library(tidyAML)
library(recipes)
library(dplyr)

Creating Model Specifications

You can generate model specifications in several ways:

# Generate all linear regression models
fast_regression_parsnip_spec_tbl(.parsnip_fns = "linear_reg")
#> # A tibble: 11 × 5
#>    .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec
#>        <int> <chr>           <chr>         <chr>        <list>    
#>  1         1 lm              regression    linear_reg   <spec[+]> 
#>  2         2 brulee          regression    linear_reg   <spec[+]> 
#>  3         3 gee             regression    linear_reg   <spec[+]> 
#>  4         4 glm             regression    linear_reg   <spec[+]> 
#>  5         5 glmer           regression    linear_reg   <spec[+]> 
#>  6         6 glmnet          regression    linear_reg   <spec[+]> 
#>  7         7 gls             regression    linear_reg   <spec[+]> 
#>  8         8 lme             regression    linear_reg   <spec[+]> 
#>  9         9 lmer            regression    linear_reg   <spec[+]> 
#> 10        10 stan            regression    linear_reg   <spec[+]> 
#> 11        11 stan_glmer      regression    linear_reg   <spec[+]>

# Select specific engines
fast_regression_parsnip_spec_tbl(.parsnip_eng = c("lm","glm"))
#> # A tibble: 3 × 5
#>   .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec
#>       <int> <chr>           <chr>         <chr>        <list>    
#> 1         1 lm              regression    linear_reg   <spec[+]> 
#> 2         2 glm             regression    linear_reg   <spec[+]> 
#> 3         3 glm             regression    poisson_reg  <spec[+]>

# Combine function and engine filters
fast_regression_parsnip_spec_tbl(
  .parsnip_eng = c("lm","glm"), 
  .parsnip_fns = "linear_reg"
)
#> # A tibble: 2 × 5
#>   .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec
#>       <int> <chr>           <chr>         <chr>        <list>    
#> 1         1 lm              regression    linear_reg   <spec[+]> 
#> 2         2 glm             regression    linear_reg   <spec[+]>

Custom Model Specifications

For more control, use create_model_spec():

create_model_spec(
  .parsnip_eng = list("lm", "glm", "glmnet"),
  .parsnip_fns = list("linear_reg", "linear_reg", "linear_reg")
)
#> # A tibble: 3 × 4
#>   .parsnip_engine .parsnip_mode .parsnip_fns .model_spec
#>   <chr>           <chr>         <chr>        <list>     
#> 1 lm              regression    linear_reg   <spec[+]>  
#> 2 glm             regression    linear_reg   <spec[+]>  
#> 3 glmnet          regression    linear_reg   <spec[+]>

Training Multiple Models

The real power comes from training multiple models at once:

# Create a recipe
rec_obj <- recipe(mpg ~ ., data = mtcars)

# Train multiple models
set.seed(42)
models_tbl <- fast_regression(
  .data = mtcars, 
  .rec_obj = rec_obj, 
  .parsnip_eng = c("lm", "glm"),
  .parsnip_fns = "linear_reg"
)

glimpse(models_tbl)
#> Rows: 2
#> Columns: 8
#> $ .model_id       <int> 1, 2
#> $ .parsnip_engine <chr> "lm", "glm"
#> $ .parsnip_mode   <chr> "regression", "regression"
#> $ .parsnip_fns    <chr> "linear_reg", "linear_reg"
#> $ model_spec      <list> [~NULL, ~NULL, NULL, regression, TRUE, NULL, lm, TRUE,…
#> $ wflw            <list> [cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, mp…
#> $ fitted_wflw     <list> [cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, mp…
#> $ pred_wflw       <list> [<tbl_df[64 x 3]>], [<tbl_df[64 x 3]>]

The function uses purrr::safely() to handle failures gracefully - if a model can’t be trained (e.g., missing dependencies), it returns NULL without stopping the entire process.

Working with Predictions

Extract predictions from trained models:

# Get predictions from all models
predictions <- extract_wflw_pred(models_tbl, 1:2)
predictions
#> # A tibble: 128 × 4
#>    .model_type     .data_category .data_type .value
#>    <chr>           <chr>          <chr>       <dbl>
#>  1 lm - linear_reg actual         actual       14.7
#>  2 lm - linear_reg actual         actual       18.7
#>  3 lm - linear_reg actual         actual       21  
#>  4 lm - linear_reg actual         actual       19.2
#>  5 lm - linear_reg actual         actual       19.2
#>  6 lm - linear_reg actual         actual       21.4
#>  7 lm - linear_reg actual         actual       32.4
#>  8 lm - linear_reg actual         actual       21.4
#>  9 lm - linear_reg actual         actual       10.4
#> 10 lm - linear_reg actual         actual       14.3
#> # ℹ 118 more rows

Analyzing Residuals

Get model residuals for diagnostic purposes:

# Extract residuals
residuals <- extract_regression_residuals(models_tbl)
residuals[[1]]  # View first model's residuals
#> # A tibble: 32 × 4
#>    .model_type     .actual .predicted  .resid
#>    <chr>             <dbl>      <dbl>   <dbl>
#>  1 lm - linear_reg    14.7       11.8  2.88  
#>  2 lm - linear_reg    18.7       19.3 -0.620 
#>  3 lm - linear_reg    21         20.9  0.0888
#>  4 lm - linear_reg    19.2       18.1  1.06  
#>  5 lm - linear_reg    19.2       18.2  1.01  
#>  6 lm - linear_reg    21.4       20.1  1.27  
#>  7 lm - linear_reg    32.4       28.1  4.33  
#>  8 lm - linear_reg    21.4       23.7 -2.34  
#>  9 lm - linear_reg    10.4       11.6 -1.19  
#> 10 lm - linear_reg    14.3       13.8  0.488 
#> # ℹ 22 more rows

Classification Example

tidyAML also excels at classification tasks. Here’s an example using the Titanic dataset:

library(tidyr)

# Prepare data
df <- Titanic |>
  as_tibble() |>
  uncount(n) |>
  mutate(across(everything(), as.factor))

# Create recipe
rec_obj <- recipe(Survived ~ ., data = df)

# Train multiple classification models
class_models <- fast_classification(
  .data = df,
  .rec_obj = rec_obj,
  .parsnip_eng = c("glm", "glmnet"),
  .parsnip_fns = "logistic_reg"
)

glimpse(class_models)
#> Rows: 1
#> Columns: 8
#> $ .model_id       <int> 1
#> $ .parsnip_engine <chr> "glm"
#> $ .parsnip_mode   <chr> "classification"
#> $ .parsnip_fns    <chr> "logistic_reg"
#> $ model_spec      <list> [~NULL, ~NULL, NULL, classification, TRUE, NULL, glm, …
#> $ wflw            <list> [Class, Sex, Age, Survived, factor, unordered, nominal…
#> $ fitted_wflw     <list> [Class, Sex, Age, Survived, factor, unordered, nominal…
#> $ pred_wflw       <list> [<tbl_df[4402 x 3]>]

Extract Classification Predictions

# Get predictions
class_predictions <- extract_wflw_pred(class_models, 1:2)
class_predictions
#> # A tibble: 4,402 × 4
#>    .model_type        .data_category .data_type .value
#>    <chr>              <chr>          <chr>      <fct> 
#>  1 glm - logistic_reg actual         actual     No    
#>  2 glm - logistic_reg actual         actual     No    
#>  3 glm - logistic_reg actual         actual     No    
#>  4 glm - logistic_reg actual         actual     No    
#>  5 glm - logistic_reg actual         actual     No    
#>  6 glm - logistic_reg actual         actual     Yes   
#>  7 glm - logistic_reg actual         actual     No    
#>  8 glm - logistic_reg actual         actual     No    
#>  9 glm - logistic_reg actual         actual     No    
#> 10 glm - logistic_reg actual         actual     No    
#> # ℹ 4,392 more rows

Key Functions

Model Generation

fast_regression() - Generate and train multiple regression models
fast_classification() - Generate and train multiple classification models
fast_regression_parsnip_spec_tbl() - Create regression model specifications
fast_classification_parsnip_spec_tbl() - Create classification model specifications
create_model_spec() - Custom model specification creation

Extractors

extract_wflw_pred() - Extract workflow predictions
extract_wflw() - Extract workflow objects
extract_wflw_fit() - Extract fitted workflows
extract_model_spec() - Extract model specifications
extract_regression_residuals() - Extract residuals from regression models
extract_tunable_params() - Extract tunable parameters

Utilities

create_splits() - Create rsample splits
core_packages() - List core package dependencies
install_deps() - Install tidyAML dependencies
load_deps() - Load required packages

Visualization

plot_regression_predictions() - Plot regression predictions
plot_regression_residuals() - Plot regression residuals

Visualization

Visualize model performance easily:

# Plot predictions
plot_regression_predictions(models_tbl)

# Plot residuals
plot_regression_residuals(models_tbl)

Documentation

Website: https://www.spsanderson.com/tidyAML/
Getting Started Vignette: vignette("getting-started", package = "tidyAML")
Function Reference: https://www.spsanderson.com/tidyAML/reference/
GitHub Repository: https://github.com/spsanderson/tidyAML
Bug Reports: https://github.com/spsanderson/tidyAML/issues

Contributing

Contributions are welcome!

Key ways to contribute: - Report bugs or request features via GitHub Issues - Submit Pull Requests for bug fixes or new features - Improve documentation or add examples - Share your use cases and feedback

Citation

If you use tidyAML in your research or work, please cite it:

citation("tidyAML")

Acknowledgments

Thanks to Garrick Aden-Buie for the package name suggestion
Built on the excellent tidymodels framework
Inspired by h2o but designed to work seamlessly with tidyverse tools

License

Need Help? - 📖 Read the Getting Started Guide - 💬 Open an Issue - ⭐ Star the repo if you find it useful!

tidyAML

Overview

Key Features

Why tidyAML?

Table of Contents

Installation

Quick Start

Regression Example

Creating Model Specifications

Custom Model Specifications

Training Multiple Models

Working with Predictions

Analyzing Residuals

Classification Example

Extract Classification Predictions

Key Functions

Model Generation

Extractors

Utilities

Visualization

Visualization

Documentation

Contributing

Citation

Acknowledgments

License