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.
Create a new preprocessing step for the recipes package following tidymodels conventions
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.