What R packages does this skill primarily use?

This skill focuses on the rsample package for general resampling and spatialsample for geographic data, both of which are core components of the tidymodels ecosystem.

Does this skill support time series data validation?

Yes, it includes specific implementations for rolling origin and sliding window resampling, which are essential for maintaining the temporal order of data during validation.

Can I use this skill to handle imbalanced datasets?

Absolutely. It provides patterns for stratified splitting and stratified cross-validation to ensure that rare outcome classes are represented proportionally in every fold.

How does it handle grouped or clustered data?

The skill includes group V-fold cross-validation patterns that ensure all observations from a specific group (like a single patient or location) remain together in the same fold.

Resampling Strategies for Tidy Models

Name: Resampling Strategies for Tidy Models
Author: choxos

bychoxos

Data Science & ML

Implements robust model validation and resampling techniques using the R tidymodels ecosystem and rsample package.

About

This skill provides a comprehensive framework for Claude to execute sophisticated data splitting and resampling workflows in R. It covers essential techniques including V-fold cross-validation, bootstrapping, and stratified splitting, while also offering specialized solutions for complex data types like time series, grouped clusters, and spatial datasets. By leveraging best practices from the rsample and spatialsample packages, this skill ensures that machine learning models are validated accurately, hyperparameters are tuned without leakage, and performance estimates remain reliable across diverse data structures.

Key Features

Stratified splitting techniques for imbalanced classification and continuous outcomes
0 GitHub stars
Specialized resampling for time series (rolling origin) and spatial data
Bootstrapping methods for calculating model confidence intervals
Comprehensive cross-validation patterns (V-fold, Repeated, LOO, Monte Carlo)
Nested resampling workflows for unbiased hyperparameter tuning

Use Cases

Validating time-series forecasting models using rolling origin windows to prevent temporal leakage
Implementing nested resampling to optimize model parameters while maintaining a separate assessment loop
Developing reliable performance estimates for predictive models on imbalanced datasets

About

Key Features

Stratified splitting techniques for imbalanced classification and continuous outcomes
0 GitHub stars
Specialized resampling for time series (rolling origin) and spatial data
Bootstrapping methods for calculating model confidence intervals
Comprehensive cross-validation patterns (V-fold, Repeated, LOO, Monte Carlo)
Nested resampling workflows for unbiased hyperparameter tuning

Use Cases

Validating time-series forecasting models using rolling origin windows to prevent temporal leakage
Implementing nested resampling to optimize model parameters while maintaining a separate assessment loop
Developing reliable performance estimates for predictive models on imbalanced datasets