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.

Learn how to use tidyverse and tidymodels functions to fit and analyze many models at once.

Use text features and tidymodels to predict the speaker of individual lines from the show, and learn how to compute model-agnostic variable importance for any kind of model.

Build two kinds of classification models and evaluate them using resampling.

Announcing our new book, to be published in the Chapman & Hall/CRC Data Science Series!

Learn how to use bootstrap aggregating to predict the duration of astronaut missions.

Explore data from the Claremont Run Project on Uncanny X-Men with bootstrap resampling.

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