Use tidy data principles to understand which kinds of occupations are most similar in terms of demographic characteristics.
Explore results of models with convenient tidymodels functions.
Check residuals and other model diagnostics for regression models trained on text features, all with tidymodels functions.
Download up-to-date city data from Chicago's open data portal and predict whether a traffic crash involved an injury with a bagged tree model.
Use tidymodels scaffolding functions for getting started quickly with commonly used models like random forests.
Use tidymodels to predict capacity for Canadian wind turbines with decision trees.
Which of the Datasaurus Dozen are easier or harder for a random forest model to identify? Learn how to use multiclass evaluation metrics to find out.
Tune a hyperparameter and then understand how to choose the best value afterward, using tidymodels for modeling the relationship between expected wins and tournament seed.
Use tidymodels for feature engineering steps like imputing missing data and subsampling for class imbalance, and build predictive models to predict the probability of survival for Himalayan climbers.
An initial version of the first eleven chapters are available today! Look for more chapters to be released in the near future.