Guide for creating new dials parameters for hyperparameter tuning. Use when a developer needs to define custom tuning parameters for models, recipes, or workflows, including quantitative parameters (continuous/integer), qualitative parameters (categorical), parameters with transformations, and data-dependent parameters requiring finalization.
Create entirely new model specifications for the parsnip package. Use when creating a fundamentally new model type (like linear_reg, boost_tree) with its constructors, registration, and engine implementations. For adding engines to existing models, use add-parsnip-engine instead.
Create a new preprocessing step for the recipes package following tidymodels conventions
Add new computational engines to existing parsnip models. Use when connecting an existing parsnip model (linear_reg, boost_tree, etc.) to a new computational backend or R package.
Guide for creating new yardstick metrics. Use when a developer needs to extend yardstick with a custom performance metric, including numeric, class, probability, ordered probability, survival (static, dynamic, integrated, linear predictor), and quantile metrics.
Build machine learning models using tidymodels for tabular data using proper data spending, resampling, and validation practices. Covers train/test splitting, cross-validation, feature engineering, model tuning, and evaluation. Use when building predictive models, comparing algorithms, or when users mention machine learning, model training, or prediction tasks.