This chapter explores machine learning within the tidyverse in R. It details “tidymodels,” a “meta-package” that streamlines various machine learning workflows, facilitating the use of advanced analytical methods in cultural research. A key focus is placed on decision trees, random forests, and the critical role of hyperparameter tuning. Using a case study from art history, the chapter demonstrates how these methods can uncover biases in the representation of artists across demographics in educational textbooks. Through guided examples, readers gain insights into the iterative nature of model tuning using hyperparameter grids and learn how to assess the accuracy of predictive models.