Using a tidymodels workflow can make many modeling tasks more convenient, but sometimes you want more flexibility and control of how to handle your modeling objects. Learn how to handle resampled workflow results and extract the quantities you are interested in.
Use spatial resampling to more accurately estimate model performance for geographic data.
Get started with tidymodels workflowsets to handle and evaluate multiple preprocessing and modeling approaches simultaneously, using pumpkin competitions.
Tune and evaluate a multiclass model with lasso regulariztion for economics working papers.
Songs on the Billboard Top 100 have many audio features. We can use data preprocessing recipes to implement dimensionality reduction and understand how these features are related.
In this screencast, focus on some tidymodels basics such as how to put together feature engineering and a model algorithm, and how to fit and predict.
Learn how to evaluate multiple feature engineering and modeling approaches with workflowsets, predicting whether a person or the computer spoke a line on Star Trek.
More xgboost with tidymodels! Learn about feature engineering to incorporate text information as indicator variables for boosted trees.
Early stopping can keep an xgboost model from overfitting.
Models like xgboost have many tuning hyperparameters, but racing methods can help identify parameter combinations that are not performing well.