What R packages does this skill focus on?

It primarily focuses on the 'tune', 'dials', 'finetune', and 'workflows' packages within the Tidymodels ecosystem for hyperparameter optimization.

What are 'racing methods' in model tuning?

Racing methods, such as ANOVA racing, evaluate model configurations across resamples and statistically eliminate poor performers early to save computation time.

How do I extract the best parameters from tuning results?

You can use functions like select_best(), select_by_one_std_err(), or select_by_pct_loss() to programmatically identify the optimal configuration based on your metrics.

Can I use this skill for parallel processing?

Yes, it includes patterns for setting up parallel backends using 'doParallel' and 'future' to significantly speed up the tuning process.

Tidyverse Model Tuning

Name: Tidyverse Model Tuning
Author: choxos

bychoxos

Ciencia de Datos y ML

Optimizes machine learning hyperparameters using the R Tidymodels ecosystem for improved model performance.

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This skill provides comprehensive implementation patterns for hyperparameter optimization within the R Tidymodels framework. It covers the entire tuning lifecycle—from marking parameters in recipes and models using the tune package to executing advanced search strategies like Bayesian optimization, Simulated Annealing, and ANOVA racing. It is designed to help data scientists efficiently navigate large parameter spaces, utilize parallel processing for speed, and select the most parsimonious models through rigorous statistical analysis of tuning results.

Características Principales

Racing methods (ANOVA and Win/Loss) for efficient early stopping
Parameter management and range customization via the dials package
0 GitHub stars
Parallel processing support using doParallel and future backends
Advanced search strategies including Grid, Bayesian, and Simulated Annealing
Automated visualization and selection of optimal model configurations

Casos de Uso

Tuning preprocessing steps such as PCA components within a tidymodels recipe
Optimizing complex XGBoost and Random Forest hyperparameters simultaneously
Speeding up large-scale model selection using statistical racing methods

Acerca de

Características Principales

Racing methods (ANOVA and Win/Loss) for efficient early stopping
Parameter management and range customization via the dials package
0 GitHub stars
Parallel processing support using doParallel and future backends
Advanced search strategies including Grid, Bayesian, and Simulated Annealing
Automated visualization and selection of optimal model configurations

Casos de Uso

Tuning preprocessing steps such as PCA components within a tidymodels recipe
Optimizing complex XGBoost and Random Forest hyperparameters simultaneously
Speeding up large-scale model selection using statistical racing methods