This chapter introduces a variety of supervised machine learning models for classification. It begins by illustrating how to validate machine learning models using methods, such as the hold-out method and cross-validation, as well as how to prepare data for hyperparameter tuning. Next, this chapter illustrates how to implement supervised learning in R using linear discriminant analysis, logistic regression, decision trees, random forests, k-nearest neighbors, and Gaussian naive Bayes. This chapter ends with a section on how to compare machine learning models with repeated cross-validation and how to deal with imbalanced datasets by using the method of random oversampling examples.