What R packages does this skill primarily use?

This skill primarily utilizes the rsample package from the tidymodels ecosystem, as well as spatialsample for geographic data validation.

How does it handle grouped or clustered data?

The skill includes Group V-Fold Cross-Validation techniques to ensure that all observations from a specific cluster (like a patient or geographic site) stay together in the same fold.

Can I use this skill for time-series forecasting?

Yes, the skill includes specific patterns for rolling origin and sliding window resampling, which are essential for valid time-series model evaluation.

Does it support stratified sampling for imbalanced classes?

Absolutely. It provides code patterns for stratified training/testing splits and stratified cross-validation to ensure outcome proportions are maintained.

Tidymodels Resampling Strategies

Name: Tidymodels Resampling Strategies
Author: choxos

bychoxos

0•

Data Science & ML

Implements advanced data resampling and validation strategies using the R tidymodels framework for robust model evaluation.

This skill provides a comprehensive toolkit for model validation in R using the rsample package. It guides users through creating robust training, testing, and validation splits, along with diverse cross-validation techniques such as V-fold, Leave-One-Out, and Monte Carlo. It specifically addresses complex data scenarios by offering specialized resampling methods for time-series, grouped, and spatial data. By following these implementation patterns, data scientists can ensure their models are rigorously evaluated, preventing overfitting and providing reliable estimates of performance across various data structures.

Key Features

01Comprehensive data splitting including three-way train/validation/test sets

02Extensive cross-validation methods (V-fold, Repeated, LOO, and Monte Carlo)

03Seamless integration with tidymodels workflows and hyperparameter tuning

04Specialized resampling for time-series, spatial, and clustered data

05Bootstrapping techniques for stability analysis and confidence intervals

060 GitHub stars

Use Cases

01Evaluating time-series forecasts using rolling origin and sliding window resamples

02Validating predictive models on imbalanced datasets using stratified sampling

03Implementing nested cross-validation for unbiased hyperparameter tuning and model selection

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent resampling-strategies

For use in Claude.ai and ChatGPT

Download Skill