Can this skill help with large-scale model tuning?

Yes, it includes patterns for parallel processing using the doParallel and future packages, as well as racing methods to efficiently handle large parameter grids.

Does it support Bayesian optimization?

Absolutely. It provides patterns for iterative Bayesian search, including custom initial points and various acquisition functions like expected improvement.

Which R packages does this skill focus on?

This skill focuses on the core Tidymodels ecosystem, specifically the tune, dials, and finetune packages for hyperparameter optimization and model selection.

How do I select the best model after tuning?

The skill covers multiple selection strategies, including selecting by the best metric, the one-standard-error rule for simpler models, or a percentage loss limit.

Tidymodels Hyperparameter Tuning

Name: Tidymodels Hyperparameter Tuning
Author: choxos

bychoxos

데이터 과학 및 ML

Optimizes machine learning models using comprehensive hyperparameter tuning patterns within the R Tidymodels ecosystem.

소개

This skill provides specialized guidance for performing advanced hyperparameter optimization in R using the Tidymodels framework. It enables Claude to implement a wide range of tuning strategies, including regular and random grid searches, space-filling designs, Bayesian optimization, and efficient racing methods. By leveraging the tune, dials, and finetune packages, the skill helps data scientists identify optimal model configurations, analyze results through visualization, and finalize workflows for production-ready machine learning models, all while adhering to best practices like parallel processing and reproducible seeding.

주요 기능

Advanced parameter selection logic using standard error and percent loss rules
Parallel processing integration using doParallel and future backends
0 GitHub stars
Resource-saving racing methods (ANOVA and Win/Loss) to prune poor configurations early
Iterative Bayesian optimization for efficient search in complex parameter spaces
Comprehensive grid search patterns including Latin hypercube and maximum entropy designs

사용 사례

Optimizing complex XGBoost or Random Forest hyperparameters for maximum predictive performance
Standardizing the transition from model tuning to final workflow deployment in R projects
Reducing computation time on large datasets by implementing statistical racing algorithms

소개

주요 기능

Advanced parameter selection logic using standard error and percent loss rules
Parallel processing integration using doParallel and future backends
0 GitHub stars
Resource-saving racing methods (ANOVA and Win/Loss) to prune poor configurations early
Iterative Bayesian optimization for efficient search in complex parameter spaces
Comprehensive grid search patterns including Latin hypercube and maximum entropy designs

사용 사례

Optimizing complex XGBoost or Random Forest hyperparameters for maximum predictive performance
Standardizing the transition from model tuning to final workflow deployment in R projects
Reducing computation time on large datasets by implementing statistical racing algorithms