Tidy Resampling Strategies for R

概要

This skill provides a comprehensive framework for model validation using the Tidymodels rsample package. It enables users to implement sophisticated resampling strategies—including cross-validation, bootstrapping, and specialized techniques for time series, grouped, and spatial data—to ensure model stability and prevent overfitting. By providing standardized implementation patterns for stratified splitting and nested resampling, it helps data scientists accurately estimate model performance and tune hyperparameters effectively within the R ecosystem.

主な機能

• Seamless integration with Tidymodels workflow and hyperparameter tuning
• Standardized data splitting with stratified sampling for imbalanced datasets
• Multi-fold and repeated cross-validation for robust performance estimation
• Specialized resampling for time series (rolling origin) and spatial data
• 0 GitHub stars
• Advanced grouped and nested resampling to handle clustered data dependencies

ユースケース

• Validating machine learning models on small or imbalanced datasets using stratified CV
• Estimating confidence intervals for model coefficients via bootstrapping
• Evaluating forecasting models using rolling-origin time series resampling