Can this skill help with hyperparameter tuning?

Yes, it includes patterns for regular grid tuning, model selection based on metrics like ROC AUC, and finalizing workflows with the best parameters.

What is the tidymodels-workflow skill?

It is a specialized capability for Claude Code that provides standardized R code patterns for the tidymodels ecosystem, covering the full machine learning lifecycle from data splitting to deployment.

Which R packages are supported by this skill?

The skill supports core tidymodels packages including rsample, recipes, parsnip, workflows, tune, yardstick, and workflowsets.

Can I use this for model deployment?

Absolutely. It provides patterns for extracting fitted workflows and saving them as RDS objects for use in production environments or Shiny applications.

Does it follow data science best practices?

Yes, it enforces best practices such as setting seeds for reproducibility, using stratified sampling for imbalanced data, and keeping test data separate until final evaluation.

Tidymodels Workflow Patterns

Name: Tidymodels Workflow Patterns
Author: choxos

bychoxos

0•

Data Science & ML

Implements comprehensive machine learning pipelines in R using the tidymodels ecosystem, from data preprocessing to model deployment.

This skill provides standardized R patterns for building robust, tidy machine learning models. It guides users through the entire data science lifecycle, including stratified data splitting with rsample, advanced feature engineering with recipes, and unified model specification with parsnip. By automating complex tasks like hyperparameter tuning with tune and model comparison via workflowsets, it ensures your R code follows industry best practices. Use this skill to minimize data leakage, implement rigorous cross-validation, and generate production-ready models that are reproducible and easy to maintain.

Key Features

01Streamlined hyperparameter optimization with grid search and automated model selection.

02Automated data splitting and resampling strategies including cross-validation and bootstrapping.

03Unified model specification for diverse engines like Ranger, XGBoost, and Linear Regression.

04Comparative analysis tools using workflow sets to evaluate multiple models simultaneously.

050 GitHub stars

06Scalable feature engineering recipes for normalization, encoding, and dimensionality reduction.

Use Cases

01Developing end-to-end predictive modeling pipelines with rigorous evaluation metrics.

02Standardizing R-based data science workflows for teams to ensure consistency and reproducibility.

03Conducting systematic model comparisons to identify the best algorithm for a specific dataset.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent tidymodels-workflow

For use in Claude.ai and ChatGPT

Key Features

01Streamlined hyperparameter optimization with grid search and automated model selection.

02Automated data splitting and resampling strategies including cross-validation and bootstrapping.

03Unified model specification for diverse engines like Ranger, XGBoost, and Linear Regression.

04Comparative analysis tools using workflow sets to evaluate multiple models simultaneously.

050 GitHub stars

06Scalable feature engineering recipes for normalization, encoding, and dimensionality reduction.

Use Cases

01Developing end-to-end predictive modeling pipelines with rigorous evaluation metrics.

02Standardizing R-based data science workflows for teams to ensure consistency and reproducibility.

03Conducting systematic model comparisons to identify the best algorithm for a specific dataset.